Analysis: Exploratory Data Analysis
Introduction
This markdown imports the processed data and conducts the exploratory data analysis. Follow the processing markdowns in the processing_code folder to generate the processed data.
This analysis examines the predictors of allocation of FEMA funds during disaster declarations. In terms of the datasets, the two outcomes of interest are Requested Amount (ReqAmt) and Obligated Amount (OblAmt).
For each variable, the following steps will be completed:
- Produce and print some numerical output (e.g. table, summary statistics)
- Create a histogram or density plot (continuous variables only)
- Scatterplot, boxplot, or other similar plots against the main outcome of interest
- Any other exploration steps that may be useful.
The steps will be numbered for each variable as specified above.
Required Packages
The following R packages are required for this script:
- here: for path setting
- tidyverse: for all packages in the Tidyverse (ggplot2, dyplr, tidyr, readr, purr, tibble, stringr, forcats)
- skimr: for data summarization
- summarytools: for overall dataframe summary
- ggplot2: for plotting data
- car: for creating QQ plots
- gtsummary: for creating tables for summary statistics / other numerical outputs
- scales: for percent calculation and axis formatting
- cowplot: for placing plots side by side
- table1: for making summary tables
- maps: for making maps
Load Processed Data
Load the data generated from the markdowns in the processing_code folder.
#define file path
data_location <- here::here("data", "processed_data", "analysisdata.rds")
#load data
EDAdata <- readRDS(data_location)Data Overview
To better understand the data, let’s use summarytools to better visualize the data.
summarytools::dfSummary(EDAdata)## Data Frame Summary
## EDAdata
## Dimensions: 326 x 25
## Duplicates: 0
##
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
## No Variable Label Stats / Values Freqs (% of Valid) Graph Valid Missing
## ---- ------------------- ---------------------------------------- ---------------------------------- --------------------- --------------------- ---------- ---------
## 1 name State Name 1. California 19 ( 5.8%) I 326 0
## [character] 2. Texas 17 ( 5.2%) I (100.0%) (0.0%)
## 3. Louisiana 16 ( 4.9%)
## 4. Florida 13 ( 4.0%)
## 5. Washington 11 ( 3.4%)
## 6. Minnesota 10 ( 3.1%)
## 7. Oklahoma 10 ( 3.1%)
## 8. South Dakota 10 ( 3.1%)
## 9. West Virginia 10 ( 3.1%)
## 10. North Carolina 9 ( 2.8%)
## [ 41 others ] 201 (61.7%) IIIIIIIIIIII
##
## 2 state State Abbreviation 1. CA 19 ( 5.8%) I 326 0
## [character] 2. TX 17 ( 5.2%) I (100.0%) (0.0%)
## 3. LA 16 ( 4.9%)
## 4. FL 13 ( 4.0%)
## 5. WA 11 ( 3.4%)
## 6. MN 10 ( 3.1%)
## 7. OK 10 ( 3.1%)
## 8. SD 10 ( 3.1%)
## 9. WV 10 ( 3.1%)
## 10. NC 9 ( 2.8%)
## [ 41 others ] 201 (61.7%) IIIIIIIIIIII
##
## 3 latitude Centroid Latitude of State Mean (sd) : 37.8 (7.2) 51 distinct values : . : 326 0
## [numeric] min < med < max: : : : (100.0%) (0.0%)
## 18.2 < 38 < 63.6 : : :
## IQR (CV) : 9.4 (0.2) . : : : .
## : : : : : :
##
## 4 longitude Centroid Longitude of State Mean (sd) : -93.7 (18.4) 44 distinct values : : 326 0
## [numeric] min < med < max: : : : (100.0%) (0.0%)
## -155.7 < -91.8 < -66.6 : : : :
## IQR (CV) : 18.7 (-0.2) . : : : :
## . : : : : : : :
##
## 5 Population State Population Mean (sd) : 9313643 (10333562) 51 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 576851 < 5706494 < 39538223 : .
## IQR (CV) : 7427864 (1.1) : : .
## : : : . . .
##
## 6 Counties Counties per State Mean (sd) : 75.3 (53.5) 46 distinct values : 326 0
## [integer] min < med < max: : . (100.0%) (0.0%)
## 1 < 67 < 254 : :
## IQR (CV) : 42 (0.7) : . : :
## : : : : . . . :
##
## 7 FEMARegion FEMA Region 1. Region I 27 ( 8.3%) I 326 0
## [character] 2. Region II 19 ( 5.8%) I (100.0%) (0.0%)
## 3. Region III 25 ( 7.7%) I
## 4. Region IV 62 (19.0%) III
## 5. Region IX 31 ( 9.5%) I
## 6. Region V 37 (11.3%) II
## 7. Region VI 54 (16.6%) III
## 8. Region VII 20 ( 6.1%) I
## 9. Region VIII 24 ( 7.4%) I
## 10. Region X 27 ( 8.3%) I
##
## 8 disasterNumber Declaration Number Mean (sd) : 4157.6 (392.8) 326 distinct values : : 326 0
## [integer] min < med < max: . . : : : (100.0%) (0.0%)
## 3346 < 4276.5 < 4619 : : : : :
## IQR (CV) : 377 (0.1) : . : : : : :
## : : : : : : :
##
## 9 declarationType Declaration Type 1. DR 261 (80.1%) IIIIIIIIIIIIIIII 326 0
## [character] 2. EM 65 (19.9%) III (100.0%) (0.0%)
##
## 10 declarationTitle Declaration Title 1. COVID-19 PANDEMIC 52 (16.0%) III 326 0
## [character] 2. SEVERE STORMS AND FLOODIN 31 ( 9.5%) I (100.0%) (0.0%)
## 3. SEVERE STORMS, TORNADOES, 24 ( 7.4%) I
## 4. HURRICANE SANDY 11 ( 3.4%)
## 5. WILDFIRES 11 ( 3.4%)
## 6. SEVERE STORMS, FLOODING, 10 ( 3.1%)
## 7. HURRICANE MATTHEW 9 ( 2.8%)
## 8. COVID-19 8 ( 2.5%)
## 9. SEVERE STORMS, TORNADOES, 8 ( 2.5%)
## 10. SEVERE STORM AND FLOODING 6 ( 1.8%)
## [ 90 others ] 156 (47.9%) IIIIIIIII
##
## 11 incidentType Incident Type 1. Biological 60 (18.4%) III 326 0
## [character] 2. Earthquake 4 ( 1.2%) (100.0%) (0.0%)
## 3. Fire 20 ( 6.1%) I
## 4. Flood 61 (18.7%) III
## 5. Hurricane 73 (22.4%) IIII
## 6. Other 15 ( 4.6%)
## 7. Severe Ice Storm 4 ( 1.2%)
## 8. Severe Storm(s) 82 (25.2%) IIIII
## 9. Tornado 7 ( 2.1%)
##
## 12 IncidentYear Incident Year Mean (sd) : 2017 (2.9) 2012 : 33 (10.1%) II 326 0
## [numeric] min < med < max: 2013 : 29 ( 8.9%) I (100.0%) (0.0%)
## 2012 < 2017 < 2021 2014 : 17 ( 5.2%) I
## IQR (CV) : 5 (0) 2015 : 28 ( 8.6%) I
## 2016 : 23 ( 7.1%) I
## 2017 : 36 (11.0%) II
## 2018 : 32 ( 9.8%) I
## 2019 : 28 ( 8.6%) I
## 2020 : 86 (26.4%) IIIII
## 2021 : 14 ( 4.3%)
##
## 13 IncidentMonth Declaration Month 1. January 71 (21.8%) IIII 326 0
## [ordered, factor] 2. February 22 ( 6.7%) I (100.0%) (0.0%)
## 3. March 22 ( 6.7%) I
## 4. April 24 ( 7.4%) I
## 5. May 22 ( 6.7%) I
## 6. June 25 ( 7.7%) I
## 7. July 13 ( 4.0%)
## 8. August 32 ( 9.8%) I
## 9. September 31 ( 9.5%) I
## 10. October 41 (12.6%) II
## [ 2 others ] 23 ( 7.1%) I
##
## 14 IncidentDuration Incident Duration Mean (sd) : 135.7 (250.3) 67 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0 < 14 < 660 :
## IQR (CV) : 41.2 (1.8) :
## : :
##
## 15 ResponseDays Response Duration Mean (sd) : 1404.1 (898.4) 229 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 70 < 1178.5 < 3588 . : . .
## IQR (CV) : 1406.5 (0.6) : : : : : .
## : : : : : : :
##
## 16 IH Individuals/Households Program Awarded 1. Not Awarded 163 (50.0%) IIIIIIIIII 326 0
## [factor] 2. Awarded 163 (50.0%) IIIIIIIIII (100.0%) (0.0%)
##
## 17 IH_pct Percent of Counties Awarded IH Mean (sd) : 0.3 (1.6) 136 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0 < 0 < 27.7 :
## IQR (CV) : 0.2 (4.7) :
## :
##
## 18 PA Public Assistance Program Awarded 1. Not Awarded 8 ( 2.5%) 326 0
## [factor] 2. Awarded 318 (97.5%) IIIIIIIIIIIIIIIIIII (100.0%) (0.0%)
##
## 19 PA_pct Percent of Counties Awarded PA Mean (sd) : 0.6 (1.6) 228 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0 < 0.4 < 27.3 :
## IQR (CV) : 0.9 (2.6) :
## :
##
## 20 HM Hazard Mitigation Program 1. Not Awarded 66 (20.2%) IIII 326 0
## [factor] 2. Awarded 260 (79.8%) IIIIIIIIIIIIIII (100.0%) (0.0%)
##
## 21 HM_pct Percent of Counties Awarded HM Mean (sd) : 0.5 (1.6) 211 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0 < 0.2 < 27.2 :
## IQR (CV) : 0.6 (3.3) :
## :
##
## 22 TotalAgencies Federal Agencies Involved Mean (sd) : 6.2 (7) 30 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 1 < 3 < 55 :
## IQR (CV) : 6 (1.1) :
## : : : .
##
## 23 FEMACSAvg Average FEMA Cost Share Mean (sd) : 1 (0.1) 128 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0.7 < 1 < 1 :
## IQR (CV) : 0.1 (0.1) :
## . : :
##
## 24 ReqAmt Requested FEMA Funds Mean (sd) : 40601806 (255138973) 326 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0 < 420063.9 < 4388540486 :
## IQR (CV) : 4700581 (6.3) :
## :
##
## 25 OblAmt Obligated FEMA Funds Mean (sd) : 40698151 (256181278) 326 distinct values : 326 0
## [numeric] min < med < max: : (100.0%) (0.0%)
## 0 < 434188.5 < 4408424297 :
## IQR (CV) : 4625581 (6.3) :
## :
## ---------------------------------------------------------------------------------------------------------------------------------------------------------------------skimr::skim(EDAdata)| Name | EDAdata |
| Number of rows | 326 |
| Number of columns | 25 |
| _______________________ | |
| Column type frequency: | |
| character | 6 |
| factor | 4 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| name | 0 | 1 | 4 | 20 | 0 | 51 | 0 |
| state | 0 | 1 | 2 | 2 | 0 | 51 | 0 |
| FEMARegion | 0 | 1 | 8 | 11 | 0 | 10 | 0 |
| declarationType | 0 | 1 | 2 | 2 | 0 | 2 | 0 |
| declarationTitle | 0 | 1 | 8 | 81 | 0 | 100 | 0 |
| incidentType | 0 | 1 | 4 | 16 | 0 | 9 | 0 |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| IncidentMonth | 0 | 1 | TRUE | 12 | Jan: 71, Oct: 41, Aug: 32, Sep: 31 |
| IH | 0 | 1 | FALSE | 2 | Not: 163, Awa: 163 |
| PA | 0 | 1 | FALSE | 2 | Awa: 318, Not: 8 |
| HM | 0 | 1 | FALSE | 2 | Awa: 260, Not: 66 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| latitude | 0 | 1 | 37.82 | 7.19 | 18.22 | 33.84 | 37.96 | 43.19 | 63.59 | ▁▆▇▁▁ |
| longitude | 0 | 1 | -93.67 | 18.41 | -155.67 | -99.90 | -91.83 | -81.16 | -66.59 | ▁▁▂▇▇ |
| Population | 0 | 1 | 9313643.01 | 10333562.39 | 576851.00 | 3011524.00 | 5706494.00 | 10439388.00 | 39538223.00 | ▇▂▁▁▁ |
| Counties | 0 | 1 | 75.26 | 53.47 | 1.00 | 46.00 | 67.00 | 88.00 | 254.00 | ▃▇▁▁▁ |
| disasterNumber | 0 | 1 | 4157.55 | 392.84 | 3346.00 | 4080.25 | 4276.50 | 4457.25 | 4619.00 | ▅▁▂▇▇ |
| IncidentYear | 0 | 1 | 2016.97 | 2.89 | 2012.00 | 2015.00 | 2017.00 | 2020.00 | 2021.00 | ▅▃▅▅▇ |
| IncidentDuration | 0 | 1 | 135.66 | 250.30 | 0.00 | 5.00 | 14.00 | 46.25 | 660.00 | ▇▁▁▁▂ |
| ResponseDays | 0 | 1 | 1404.07 | 898.44 | 70.04 | 660.00 | 1178.54 | 2066.51 | 3588.00 | ▇▅▅▃▂ |
| IH_pct | 0 | 1 | 0.34 | 1.59 | 0.00 | 0.00 | 0.00 | 0.24 | 27.69 | ▇▁▁▁▁ |
| PA_pct | 0 | 1 | 0.59 | 1.56 | 0.00 | 0.14 | 0.36 | 1.00 | 27.31 | ▇▁▁▁▁ |
| HM_pct | 0 | 1 | 0.48 | 1.56 | 0.00 | 0.04 | 0.21 | 0.60 | 27.25 | ▇▁▁▁▁ |
| TotalAgencies | 0 | 1 | 6.23 | 7.03 | 1.00 | 2.00 | 3.00 | 8.00 | 55.00 | ▇▂▁▁▁ |
| FEMACSAvg | 0 | 1 | 0.96 | 0.06 | 0.68 | 0.95 | 1.00 | 1.00 | 1.00 | ▁▁▁▂▇ |
| ReqAmt | 0 | 1 | 40601806.50 | 255138972.55 | 0.00 | 51024.33 | 420063.94 | 4751605.15 | 4388540486.39 | ▇▁▁▁▁ |
| OblAmt | 0 | 1 | 40698150.73 | 256181277.85 | 0.00 | 51024.33 | 434188.48 | 4676605.15 | 4408424297.39 | ▇▁▁▁▁ |
Looking at the output of summarytools::dfsummary()
- There are 445 distinct disaster numbers across 2012 - 2021
OblAmtranges from -$0 to $4,408,424,297ReqAmtranges from -$0 to $4,388,540,486- There are 9 possible incident types
- There is a clear effect of both incident month and incident year
Before moving through the entire EDA, I want to create a secondary dataset with the outliers removed. This is the data actually used in the ML analysis, so this is the dataset that should be used for the EDA.
EDAdata_outliers <- EDAdata %>%
dplyr::filter(log(ReqAmt) > 0)Let’s dive a little deeper into each variable.
Outcome of Interest: Requested FEMA Funds
The first of two primary outcomes of interest is the total amount of funds the state requested from the federal government for a disaster declaration.
#summary statistics
base::summary(EDAdata_outliers$ReqAmt)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 78 52294 443989 40978913 4987600 4388540486#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = ReqAmt)) +
geom_density()
#try a log transformation out of curiosity
ggplot2::ggplot(data = EDAdata_outliers, aes(x = log(ReqAmt))) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = ReqAmt)) +
geom_boxplot()
#crop to $1M to get a better idea
ggplot2::ggplot(data = EDAdata_outliers, aes(y = ReqAmt)) +
geom_boxplot()
#log transformed boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = log(ReqAmt))) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$ReqAmt)
## [1] 233 192These outputs show the requested amounts do not follow a normal distribution, which is to be expected given the broad range of disasters. We may need to remove outliers in the analysis, but for now, we’ll leave them to capture the true nature of disaster declarations. The largest outlier is likely Hurricane Maria. The log transformation does a better job of showing the distribution of the data, so we may need to consider an exponential model in the analysis. The transformed and untransformed figures suggest there are significant tails in the data.
Outcome of Interest: Obligated FEMA Funds
The alternative outcome of interest is the funding obligated from FEMA. While this may be the same as ReqAmt (especially for large-scale disasters), FEMA doesn’t always pay the amount the states request.
#summary statistics
base::summary(EDAdata_outliers$OblAmt)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 78 52294 464902 41076152 4937600 4408424297#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = OblAmt)) +
geom_density()
#try a log transformation out of curiosity
ggplot2::ggplot(data = EDAdata_outliers, aes(x = log(OblAmt))) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = OblAmt)) +
geom_boxplot()
#crop to $1M to get a better idea
ggplot2::ggplot(data = EDAdata_outliers, aes(y = OblAmt)) +
geom_boxplot()
#log transformed boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = log(OblAmt))) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$OblAmt)
## [1] 233 192It appears that obligated funds and requested funds have similar distributions and characteristics. The same commentary about the requested funds analysis applies here as well.
Before moving on, I want to create a plot of Requested vs obligated Funds to understand the relationship between the two outcomes:
#including all points
funds_comp <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = log(ReqAmt), y = log(OblAmt))) +
geom_smooth(color = "#440154",
size = 2) +
geom_point(color = "#1f9e89",
alpha = 0.5) +
scale_y_continuous(expand = c(0,0),
limits = c(0, NA)) +
scale_x_continuous(expand = c(0,0),
limits = c(0, NA)) +
labs(x = "log(Requested Funding)",
y = "log(Obligated Funding)")
funds_comp## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
#save file
funds_comp_fig_file = here("results","funds_comp.png")
ggsave(filename = funds_comp_fig_file, plot = funds_comp)## Saving 7 x 5 in image
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'I also want a density plot of both log-transformed outcomes on one plot, so that can go here as well.
#convert to long format in order to be able to plot both outcomes with same variable
data_long <- gather(EDAdata_outliers, FundType, funds, ReqAmt:OblAmt, factor_key=TRUE)## Warning: attributes are not identical across measure variables;
## they will be droppeddata_long## name state latitude longitude Population Counties
## 1 Alaska AK 63.58875 -154.49306 733391 34
## 2 Alaska AK 63.58875 -154.49306 733391 34
## 3 Alaska AK 63.58875 -154.49306 733391 34
## 4 Alaska AK 63.58875 -154.49306 733391 34
## 5 Alabama AL 32.31823 -86.90230 5024279 67
## 6 Alabama AL 32.31823 -86.90230 5024279 67
## 7 Alabama AL 32.31823 -86.90230 5024279 67
## 8 Alabama AL 32.31823 -86.90230 5024279 67
## 9 Arkansas AR 35.20105 -91.83183 3011524 75
## 10 Arkansas AR 35.20105 -91.83183 3011524 75
## 11 Arkansas AR 35.20105 -91.83183 3011524 75
## 12 Arkansas AR 35.20105 -91.83183 3011524 75
## 13 Arkansas AR 35.20105 -91.83183 3011524 75
## 14 Arkansas AR 35.20105 -91.83183 3011524 75
## 15 Arkansas AR 35.20105 -91.83183 3011524 75
## 16 Arkansas AR 35.20105 -91.83183 3011524 75
## 17 Arizona AZ 34.04893 -111.09373 7151502 15
## 18 Arizona AZ 34.04893 -111.09373 7151502 15
## 19 Arizona AZ 34.04893 -111.09373 7151502 15
## 20 California CA 36.77826 -119.41793 39538223 58
## 21 California CA 36.77826 -119.41793 39538223 58
## 22 California CA 36.77826 -119.41793 39538223 58
## 23 California CA 36.77826 -119.41793 39538223 58
## 24 California CA 36.77826 -119.41793 39538223 58
## 25 California CA 36.77826 -119.41793 39538223 58
## 26 California CA 36.77826 -119.41793 39538223 58
## 27 California CA 36.77826 -119.41793 39538223 58
## 28 California CA 36.77826 -119.41793 39538223 58
## 29 California CA 36.77826 -119.41793 39538223 58
## 30 California CA 36.77826 -119.41793 39538223 58
## 31 California CA 36.77826 -119.41793 39538223 58
## 32 California CA 36.77826 -119.41793 39538223 58
## 33 California CA 36.77826 -119.41793 39538223 58
## 34 California CA 36.77826 -119.41793 39538223 58
## 35 California CA 36.77826 -119.41793 39538223 58
## 36 California CA 36.77826 -119.41793 39538223 58
## 37 California CA 36.77826 -119.41793 39538223 58
## 38 California CA 36.77826 -119.41793 39538223 58
## 39 Colorado CO 39.55005 -105.78207 5773714 64
## 40 Colorado CO 39.55005 -105.78207 5773714 64
## 41 Colorado CO 39.55005 -105.78207 5773714 64
## 42 Colorado CO 39.55005 -105.78207 5773714 64
## 43 Colorado CO 39.55005 -105.78207 5773714 64
## 44 Connecticut CT 41.60322 -73.08775 3605944 8
## 45 Connecticut CT 41.60322 -73.08775 3605944 8
## 46 Connecticut CT 41.60322 -73.08775 3605944 8
## 47 Connecticut CT 41.60322 -73.08775 3605944 8
## 48 Connecticut CT 41.60322 -73.08775 3605944 8
## 49 Connecticut CT 41.60322 -73.08775 3605944 8
## 50 District of Columbia DC 38.90599 -77.03342 689545 1
## 51 District of Columbia DC 38.90599 -77.03342 689545 1
## 52 Delaware DE 38.91083 -75.52767 989948 3
## 53 Florida FL 27.66483 -81.51575 21538187 67
## 54 Florida FL 27.66483 -81.51575 21538187 67
## 55 Florida FL 27.66483 -81.51575 21538187 67
## 56 Florida FL 27.66483 -81.51575 21538187 67
## 57 Florida FL 27.66483 -81.51575 21538187 67
## 58 Florida FL 27.66483 -81.51575 21538187 67
## 59 Florida FL 27.66483 -81.51575 21538187 67
## 60 Florida FL 27.66483 -81.51575 21538187 67
## 61 Florida FL 27.66483 -81.51575 21538187 67
## 62 Florida FL 27.66483 -81.51575 21538187 67
## 63 Florida FL 27.66483 -81.51575 21538187 67
## 64 Florida FL 27.66483 -81.51575 21538187 67
## 65 Florida FL 27.66483 -81.51575 21538187 67
## 66 Georgia GA 32.15743 -82.90712 10711908 159
## 67 Georgia GA 32.15743 -82.90712 10711908 159
## 68 Georgia GA 32.15743 -82.90712 10711908 159
## 69 Georgia GA 32.15743 -82.90712 10711908 159
## 70 Georgia GA 32.15743 -82.90712 10711908 159
## 71 Georgia GA 32.15743 -82.90712 10711908 159
## 72 Georgia GA 32.15743 -82.90712 10711908 159
## 73 Georgia GA 32.15743 -82.90712 10711908 159
## 74 Hawaii HI 19.89868 -155.66586 1455271 5
## 75 Hawaii HI 19.89868 -155.66586 1455271 5
## 76 Hawaii HI 19.89868 -155.66586 1455271 5
## 77 Hawaii HI 19.89868 -155.66586 1455271 5
## 78 Hawaii HI 19.89868 -155.66586 1455271 5
## 79 Iowa IA 41.87800 -93.09770 3190369 99
## 80 Iowa IA 41.87800 -93.09770 3190369 99
## 81 Iowa IA 41.87800 -93.09770 3190369 99
## 82 Iowa IA 41.87800 -93.09770 3190369 99
## 83 Iowa IA 41.87800 -93.09770 3190369 99
## 84 Idaho ID 44.06820 -114.74204 1839106 44
## 85 Idaho ID 44.06820 -114.74204 1839106 44
## 86 Idaho ID 44.06820 -114.74204 1839106 44
## 87 Idaho ID 44.06820 -114.74204 1839106 44
## 88 Illinois IL 40.63312 -89.39853 12812508 102
## 89 Illinois IL 40.63312 -89.39853 12812508 102
## 90 Illinois IL 40.63312 -89.39853 12812508 102
## 91 Illinois IL 40.63312 -89.39853 12812508 102
## 92 Indiana IN 40.55122 -85.60236 6785528 92
## 93 Indiana IN 40.55122 -85.60236 6785528 92
## 94 Indiana IN 40.55122 -85.60236 6785528 92
## 95 Kansas KS 39.01190 -98.48425 2937880 105
## 96 Kansas KS 39.01190 -98.48425 2937880 105
## 97 Kansas KS 39.01190 -98.48425 2937880 105
## 98 Kentucky KY 37.83933 -84.27002 4505836 120
## 99 Kentucky KY 37.83933 -84.27002 4505836 120
## 100 Kentucky KY 37.83933 -84.27002 4505836 120
## 101 Kentucky KY 37.83933 -84.27002 4505836 120
## 102 Kentucky KY 37.83933 -84.27002 4505836 120
## 103 Louisiana LA 31.24482 -92.14502 4657757 64
## 104 Louisiana LA 31.24482 -92.14502 4657757 64
## 105 Louisiana LA 31.24482 -92.14502 4657757 64
## 106 Louisiana LA 31.24482 -92.14502 4657757 64
## 107 Louisiana LA 31.24482 -92.14502 4657757 64
## 108 Louisiana LA 31.24482 -92.14502 4657757 64
## 109 Louisiana LA 31.24482 -92.14502 4657757 64
## 110 Louisiana LA 31.24482 -92.14502 4657757 64
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## 120 Massachusetts MA 42.40721 -71.38244 7029917 14
## 121 Massachusetts MA 42.40721 -71.38244 7029917 14
## 122 Massachusetts MA 42.40721 -71.38244 7029917 14
## 123 Massachusetts MA 42.40721 -71.38244 7029917 14
## 124 Massachusetts MA 42.40721 -71.38244 7029917 14
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## 126 Maine ME 45.25378 -69.44547 1362359 16
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## 128 Michigan MI 44.31484 -85.60236 10077331 83
## 129 Michigan MI 44.31484 -85.60236 10077331 83
## 130 Michigan MI 44.31484 -85.60236 10077331 83
## 131 Michigan MI 44.31484 -85.60236 10077331 83
## 132 Michigan MI 44.31484 -85.60236 10077331 83
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## 134 Minnesota MN 46.72955 -94.68590 5706494 87
## 135 Minnesota MN 46.72955 -94.68590 5706494 87
## 136 Minnesota MN 46.72955 -94.68590 5706494 87
## 137 Minnesota MN 46.72955 -94.68590 5706494 87
## 138 Minnesota MN 46.72955 -94.68590 5706494 87
## 139 Minnesota MN 46.72955 -94.68590 5706494 87
## 140 Minnesota MN 46.72955 -94.68590 5706494 87
## 141 Minnesota MN 46.72955 -94.68590 5706494 87
## 142 Minnesota MN 46.72955 -94.68590 5706494 87
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## 144 Missouri MO 37.96425 -91.83183 6154913 115
## 145 Missouri MO 37.96425 -91.83183 6154913 115
## 146 Missouri MO 37.96425 -91.83183 6154913 115
## 147 Missouri MO 37.96425 -91.83183 6154913 115
## 148 Missouri MO 37.96425 -91.83183 6154913 115
## 149 Missouri MO 37.96425 -91.83183 6154913 115
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## 151 Mississippi MS 32.35467 -89.39853 2961279 82
## 152 Mississippi MS 32.35467 -89.39853 2961279 82
## 153 Mississippi MS 32.35467 -89.39853 2961279 82
## 154 Mississippi MS 32.35467 -89.39853 2961279 82
## 155 Mississippi MS 32.35467 -89.39853 2961279 82
## 156 Mississippi MS 32.35467 -89.39853 2961279 82
## 157 Mississippi MS 32.35467 -89.39853 2961279 82
## 158 Montana MT 46.87968 -110.36257 1084225 56
## 159 Montana MT 46.87968 -110.36257 1084225 56
## 160 North Carolina NC 35.75957 -79.01930 10439388 100
## 161 North Carolina NC 35.75957 -79.01930 10439388 100
## 162 North Carolina NC 35.75957 -79.01930 10439388 100
## 163 North Carolina NC 35.75957 -79.01930 10439388 100
## 164 North Carolina NC 35.75957 -79.01930 10439388 100
## 165 North Carolina NC 35.75957 -79.01930 10439388 100
## 166 North Carolina NC 35.75957 -79.01930 10439388 100
## 167 North Carolina NC 35.75957 -79.01930 10439388 100
## 168 North Carolina NC 35.75957 -79.01930 10439388 100
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## 170 North Dakota ND 47.55149 -101.00201 779094 53
## 171 North Dakota ND 47.55149 -101.00201 779094 53
## 172 North Dakota ND 47.55149 -101.00201 779094 53
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## 174 Nebraska NE 41.49254 -99.90181 1961504 93
## 175 Nebraska NE 41.49254 -99.90181 1961504 93
## 176 Nebraska NE 41.49254 -99.90181 1961504 93
## 177 Nebraska NE 41.49254 -99.90181 1961504 93
## 178 New Hampshire NH 43.19385 -71.57240 1377529 10
## 179 New Hampshire NH 43.19385 -71.57240 1377529 10
## 180 New Hampshire NH 43.19385 -71.57240 1377529 10
## 181 New Hampshire NH 43.19385 -71.57240 1377529 10
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## 183 New Jersey NJ 40.05832 -74.40566 9288994 21
## 184 New Jersey NJ 40.05832 -74.40566 9288994 21
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## 186 New Mexico NM 34.97273 -105.03236 2117522 33
## 187 New Mexico NM 34.97273 -105.03236 2117522 33
## 188 Nevada NV 38.80261 -116.41939 3104614 17
## 189 Nevada NV 38.80261 -116.41939 3104614 17
## 190 Nevada NV 38.80261 -116.41939 3104614 17
## 191 New York NY 43.29943 -74.21793 20201249 62
## 192 New York NY 43.29943 -74.21793 20201249 62
## 193 New York NY 43.29943 -74.21793 20201249 62
## 194 New York NY 43.29943 -74.21793 20201249 62
## 195 New York NY 43.29943 -74.21793 20201249 62
## 196 New York NY 43.29943 -74.21793 20201249 62
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## 198 Ohio OH 40.41729 -82.90712 11799448 88
## 199 Ohio OH 40.41729 -82.90712 11799448 88
## 200 Ohio OH 40.41729 -82.90712 11799448 88
## 201 Ohio OH 40.41729 -82.90712 11799448 88
## 202 Ohio OH 40.41729 -82.90712 11799448 88
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## 204 Oklahoma OK 35.00775 -97.09288 3959353 77
## 205 Oklahoma OK 35.00775 -97.09288 3959353 77
## 206 Oklahoma OK 35.00775 -97.09288 3959353 77
## 207 Oklahoma OK 35.00775 -97.09288 3959353 77
## 208 Oklahoma OK 35.00775 -97.09288 3959353 77
## 209 Oklahoma OK 35.00775 -97.09288 3959353 77
## 210 Oklahoma OK 35.00775 -97.09288 3959353 77
## 211 Oklahoma OK 35.00775 -97.09288 3959353 77
## 212 Oklahoma OK 35.00775 -97.09288 3959353 77
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## 214 Oregon OR 43.80413 -120.55420 4237256 36
## 215 Oregon OR 43.80413 -120.55420 4237256 36
## 216 Oregon OR 43.80413 -120.55420 4237256 36
## 217 Oregon OR 43.80413 -120.55420 4237256 36
## 218 Oregon OR 43.80413 -120.55420 4237256 36
## 219 Oregon OR 43.80413 -120.55420 4237256 36
## 220 Oregon OR 43.80413 -120.55420 4237256 36
## 221 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 222 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 223 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 224 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 225 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 226 Pennsylvania PA 41.20332 -77.19452 13002700 67
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## 228 Puerto Rico PR 18.22083 -66.59015 3285874 78
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## 235 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 236 Rhode Island RI 41.58010 -71.47743 1097379 5
## 237 Rhode Island RI 41.58010 -71.47743 1097379 5
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## 240 Rhode Island RI 41.58010 -71.47743 1097379 5
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## 242 South Carolina SC 33.83608 -81.16372 5118425 46
## 243 South Carolina SC 33.83608 -81.16372 5118425 46
## 244 South Carolina SC 33.83608 -81.16372 5118425 46
## 245 South Carolina SC 33.83608 -81.16372 5118425 46
## 246 South Carolina SC 33.83608 -81.16372 5118425 46
## 247 South Carolina SC 33.83608 -81.16372 5118425 46
## 248 South Carolina SC 33.83608 -81.16372 5118425 46
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## 250 South Dakota SD 43.96952 -99.90181 886667 66
## 251 South Dakota SD 43.96952 -99.90181 886667 66
## 252 South Dakota SD 43.96952 -99.90181 886667 66
## 253 South Dakota SD 43.96952 -99.90181 886667 66
## 254 South Dakota SD 43.96952 -99.90181 886667 66
## 255 South Dakota SD 43.96952 -99.90181 886667 66
## 256 South Dakota SD 43.96952 -99.90181 886667 66
## 257 South Dakota SD 43.96952 -99.90181 886667 66
## 258 South Dakota SD 43.96952 -99.90181 886667 66
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## 260 Tennessee TN 35.51749 -86.58045 6910840 95
## 261 Tennessee TN 35.51749 -86.58045 6910840 95
## 262 Tennessee TN 35.51749 -86.58045 6910840 95
## 263 Tennessee TN 35.51749 -86.58045 6910840 95
## 264 Tennessee TN 35.51749 -86.58045 6910840 95
## 265 Tennessee TN 35.51749 -86.58045 6910840 95
## 266 Texas TX 31.96860 -99.90181 29145505 254
## 267 Texas TX 31.96860 -99.90181 29145505 254
## 268 Texas TX 31.96860 -99.90181 29145505 254
## 269 Texas TX 31.96860 -99.90181 29145505 254
## 270 Texas TX 31.96860 -99.90181 29145505 254
## 271 Texas TX 31.96860 -99.90181 29145505 254
## 272 Texas TX 31.96860 -99.90181 29145505 254
## 273 Texas TX 31.96860 -99.90181 29145505 254
## 274 Texas TX 31.96860 -99.90181 29145505 254
## 275 Texas TX 31.96860 -99.90181 29145505 254
## 276 Texas TX 31.96860 -99.90181 29145505 254
## 277 Texas TX 31.96860 -99.90181 29145505 254
## 278 Texas TX 31.96860 -99.90181 29145505 254
## 279 Texas TX 31.96860 -99.90181 29145505 254
## 280 Texas TX 31.96860 -99.90181 29145505 254
## 281 Texas TX 31.96860 -99.90181 29145505 254
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## 284 Virginia VA 37.43157 -78.65689 8631393 135
## 285 Virginia VA 37.43157 -78.65689 8631393 135
## 286 Virginia VA 37.43157 -78.65689 8631393 135
## 287 Virginia VA 37.43157 -78.65689 8631393 135
## 288 Virginia VA 37.43157 -78.65689 8631393 135
## 289 Vermont VT 44.55880 -72.57784 643077 14
## 290 Vermont VT 44.55880 -72.57784 643077 14
## 291 Vermont VT 44.55880 -72.57784 643077 14
## 292 Vermont VT 44.55880 -72.57784 643077 14
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## 294 Washington WA 47.75107 -120.74014 7705281 39
## 295 Washington WA 47.75107 -120.74014 7705281 39
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## 297 Washington WA 47.75107 -120.74014 7705281 39
## 298 Washington WA 47.75107 -120.74014 7705281 39
## 299 Washington WA 47.75107 -120.74014 7705281 39
## 300 Washington WA 47.75107 -120.74014 7705281 39
## 301 Washington WA 47.75107 -120.74014 7705281 39
## 302 Washington WA 47.75107 -120.74014 7705281 39
## 303 Washington WA 47.75107 -120.74014 7705281 39
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## 305 Wisconsin WI 43.78444 -88.78787 5893718 72
## 306 Wisconsin WI 43.78444 -88.78787 5893718 72
## 307 Wisconsin WI 43.78444 -88.78787 5893718 72
## 308 Wisconsin WI 43.78444 -88.78787 5893718 72
## 309 Wisconsin WI 43.78444 -88.78787 5893718 72
## 310 Wisconsin WI 43.78444 -88.78787 5893718 72
## 311 Wisconsin WI 43.78444 -88.78787 5893718 72
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## 313 West Virginia WV 38.59763 -80.45490 1793716 55
## 314 West Virginia WV 38.59763 -80.45490 1793716 55
## 315 West Virginia WV 38.59763 -80.45490 1793716 55
## 316 West Virginia WV 38.59763 -80.45490 1793716 55
## 317 West Virginia WV 38.59763 -80.45490 1793716 55
## 318 West Virginia WV 38.59763 -80.45490 1793716 55
## 319 West Virginia WV 38.59763 -80.45490 1793716 55
## 320 West Virginia WV 38.59763 -80.45490 1793716 55
## 321 West Virginia WV 38.59763 -80.45490 1793716 55
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## 323 Wyoming WY 43.07597 -107.29028 576851 23
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## 325 Alaska AK 63.58875 -154.49306 733391 34
## 326 Alaska AK 63.58875 -154.49306 733391 34
## 327 Alaska AK 63.58875 -154.49306 733391 34
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## 329 Alabama AL 32.31823 -86.90230 5024279 67
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## 333 Arkansas AR 35.20105 -91.83183 3011524 75
## 334 Arkansas AR 35.20105 -91.83183 3011524 75
## 335 Arkansas AR 35.20105 -91.83183 3011524 75
## 336 Arkansas AR 35.20105 -91.83183 3011524 75
## 337 Arkansas AR 35.20105 -91.83183 3011524 75
## 338 Arkansas AR 35.20105 -91.83183 3011524 75
## 339 Arkansas AR 35.20105 -91.83183 3011524 75
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## 341 Arizona AZ 34.04893 -111.09373 7151502 15
## 342 Arizona AZ 34.04893 -111.09373 7151502 15
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## 344 California CA 36.77826 -119.41793 39538223 58
## 345 California CA 36.77826 -119.41793 39538223 58
## 346 California CA 36.77826 -119.41793 39538223 58
## 347 California CA 36.77826 -119.41793 39538223 58
## 348 California CA 36.77826 -119.41793 39538223 58
## 349 California CA 36.77826 -119.41793 39538223 58
## 350 California CA 36.77826 -119.41793 39538223 58
## 351 California CA 36.77826 -119.41793 39538223 58
## 352 California CA 36.77826 -119.41793 39538223 58
## 353 California CA 36.77826 -119.41793 39538223 58
## 354 California CA 36.77826 -119.41793 39538223 58
## 355 California CA 36.77826 -119.41793 39538223 58
## 356 California CA 36.77826 -119.41793 39538223 58
## 357 California CA 36.77826 -119.41793 39538223 58
## 358 California CA 36.77826 -119.41793 39538223 58
## 359 California CA 36.77826 -119.41793 39538223 58
## 360 California CA 36.77826 -119.41793 39538223 58
## 361 California CA 36.77826 -119.41793 39538223 58
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## 363 Colorado CO 39.55005 -105.78207 5773714 64
## 364 Colorado CO 39.55005 -105.78207 5773714 64
## 365 Colorado CO 39.55005 -105.78207 5773714 64
## 366 Colorado CO 39.55005 -105.78207 5773714 64
## 367 Connecticut CT 41.60322 -73.08775 3605944 8
## 368 Connecticut CT 41.60322 -73.08775 3605944 8
## 369 Connecticut CT 41.60322 -73.08775 3605944 8
## 370 Connecticut CT 41.60322 -73.08775 3605944 8
## 371 Connecticut CT 41.60322 -73.08775 3605944 8
## 372 Connecticut CT 41.60322 -73.08775 3605944 8
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## 374 District of Columbia DC 38.90599 -77.03342 689545 1
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## 381 Florida FL 27.66483 -81.51575 21538187 67
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## 383 Florida FL 27.66483 -81.51575 21538187 67
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## 390 Georgia GA 32.15743 -82.90712 10711908 159
## 391 Georgia GA 32.15743 -82.90712 10711908 159
## 392 Georgia GA 32.15743 -82.90712 10711908 159
## 393 Georgia GA 32.15743 -82.90712 10711908 159
## 394 Georgia GA 32.15743 -82.90712 10711908 159
## 395 Georgia GA 32.15743 -82.90712 10711908 159
## 396 Georgia GA 32.15743 -82.90712 10711908 159
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## 398 Hawaii HI 19.89868 -155.66586 1455271 5
## 399 Hawaii HI 19.89868 -155.66586 1455271 5
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## 403 Iowa IA 41.87800 -93.09770 3190369 99
## 404 Iowa IA 41.87800 -93.09770 3190369 99
## 405 Iowa IA 41.87800 -93.09770 3190369 99
## 406 Iowa IA 41.87800 -93.09770 3190369 99
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## 408 Idaho ID 44.06820 -114.74204 1839106 44
## 409 Idaho ID 44.06820 -114.74204 1839106 44
## 410 Idaho ID 44.06820 -114.74204 1839106 44
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## 412 Illinois IL 40.63312 -89.39853 12812508 102
## 413 Illinois IL 40.63312 -89.39853 12812508 102
## 414 Illinois IL 40.63312 -89.39853 12812508 102
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## 416 Indiana IN 40.55122 -85.60236 6785528 92
## 417 Indiana IN 40.55122 -85.60236 6785528 92
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## 419 Kansas KS 39.01190 -98.48425 2937880 105
## 420 Kansas KS 39.01190 -98.48425 2937880 105
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## 422 Kentucky KY 37.83933 -84.27002 4505836 120
## 423 Kentucky KY 37.83933 -84.27002 4505836 120
## 424 Kentucky KY 37.83933 -84.27002 4505836 120
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## 427 Louisiana LA 31.24482 -92.14502 4657757 64
## 428 Louisiana LA 31.24482 -92.14502 4657757 64
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## 430 Louisiana LA 31.24482 -92.14502 4657757 64
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## 434 Louisiana LA 31.24482 -92.14502 4657757 64
## 435 Louisiana LA 31.24482 -92.14502 4657757 64
## 436 Louisiana LA 31.24482 -92.14502 4657757 64
## 437 Louisiana LA 31.24482 -92.14502 4657757 64
## 438 Louisiana LA 31.24482 -92.14502 4657757 64
## 439 Louisiana LA 31.24482 -92.14502 4657757 64
## 440 Louisiana LA 31.24482 -92.14502 4657757 64
## 441 Louisiana LA 31.24482 -92.14502 4657757 64
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## 443 Massachusetts MA 42.40721 -71.38244 7029917 14
## 444 Massachusetts MA 42.40721 -71.38244 7029917 14
## 445 Massachusetts MA 42.40721 -71.38244 7029917 14
## 446 Massachusetts MA 42.40721 -71.38244 7029917 14
## 447 Massachusetts MA 42.40721 -71.38244 7029917 14
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## 451 Michigan MI 44.31484 -85.60236 10077331 83
## 452 Michigan MI 44.31484 -85.60236 10077331 83
## 453 Michigan MI 44.31484 -85.60236 10077331 83
## 454 Michigan MI 44.31484 -85.60236 10077331 83
## 455 Michigan MI 44.31484 -85.60236 10077331 83
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## 457 Minnesota MN 46.72955 -94.68590 5706494 87
## 458 Minnesota MN 46.72955 -94.68590 5706494 87
## 459 Minnesota MN 46.72955 -94.68590 5706494 87
## 460 Minnesota MN 46.72955 -94.68590 5706494 87
## 461 Minnesota MN 46.72955 -94.68590 5706494 87
## 462 Minnesota MN 46.72955 -94.68590 5706494 87
## 463 Minnesota MN 46.72955 -94.68590 5706494 87
## 464 Minnesota MN 46.72955 -94.68590 5706494 87
## 465 Minnesota MN 46.72955 -94.68590 5706494 87
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## 467 Missouri MO 37.96425 -91.83183 6154913 115
## 468 Missouri MO 37.96425 -91.83183 6154913 115
## 469 Missouri MO 37.96425 -91.83183 6154913 115
## 470 Missouri MO 37.96425 -91.83183 6154913 115
## 471 Missouri MO 37.96425 -91.83183 6154913 115
## 472 Missouri MO 37.96425 -91.83183 6154913 115
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## 474 Mississippi MS 32.35467 -89.39853 2961279 82
## 475 Mississippi MS 32.35467 -89.39853 2961279 82
## 476 Mississippi MS 32.35467 -89.39853 2961279 82
## 477 Mississippi MS 32.35467 -89.39853 2961279 82
## 478 Mississippi MS 32.35467 -89.39853 2961279 82
## 479 Mississippi MS 32.35467 -89.39853 2961279 82
## 480 Mississippi MS 32.35467 -89.39853 2961279 82
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## 482 Montana MT 46.87968 -110.36257 1084225 56
## 483 North Carolina NC 35.75957 -79.01930 10439388 100
## 484 North Carolina NC 35.75957 -79.01930 10439388 100
## 485 North Carolina NC 35.75957 -79.01930 10439388 100
## 486 North Carolina NC 35.75957 -79.01930 10439388 100
## 487 North Carolina NC 35.75957 -79.01930 10439388 100
## 488 North Carolina NC 35.75957 -79.01930 10439388 100
## 489 North Carolina NC 35.75957 -79.01930 10439388 100
## 490 North Carolina NC 35.75957 -79.01930 10439388 100
## 491 North Carolina NC 35.75957 -79.01930 10439388 100
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## 493 North Dakota ND 47.55149 -101.00201 779094 53
## 494 North Dakota ND 47.55149 -101.00201 779094 53
## 495 North Dakota ND 47.55149 -101.00201 779094 53
## 496 Nebraska NE 41.49254 -99.90181 1961504 93
## 497 Nebraska NE 41.49254 -99.90181 1961504 93
## 498 Nebraska NE 41.49254 -99.90181 1961504 93
## 499 Nebraska NE 41.49254 -99.90181 1961504 93
## 500 Nebraska NE 41.49254 -99.90181 1961504 93
## 501 New Hampshire NH 43.19385 -71.57240 1377529 10
## 502 New Hampshire NH 43.19385 -71.57240 1377529 10
## 503 New Hampshire NH 43.19385 -71.57240 1377529 10
## 504 New Hampshire NH 43.19385 -71.57240 1377529 10
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## 506 New Jersey NJ 40.05832 -74.40566 9288994 21
## 507 New Jersey NJ 40.05832 -74.40566 9288994 21
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## 509 New Mexico NM 34.97273 -105.03236 2117522 33
## 510 New Mexico NM 34.97273 -105.03236 2117522 33
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## 512 Nevada NV 38.80261 -116.41939 3104614 17
## 513 Nevada NV 38.80261 -116.41939 3104614 17
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## 515 New York NY 43.29943 -74.21793 20201249 62
## 516 New York NY 43.29943 -74.21793 20201249 62
## 517 New York NY 43.29943 -74.21793 20201249 62
## 518 New York NY 43.29943 -74.21793 20201249 62
## 519 New York NY 43.29943 -74.21793 20201249 62
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## 522 Ohio OH 40.41729 -82.90712 11799448 88
## 523 Ohio OH 40.41729 -82.90712 11799448 88
## 524 Ohio OH 40.41729 -82.90712 11799448 88
## 525 Ohio OH 40.41729 -82.90712 11799448 88
## 526 Oklahoma OK 35.00775 -97.09288 3959353 77
## 527 Oklahoma OK 35.00775 -97.09288 3959353 77
## 528 Oklahoma OK 35.00775 -97.09288 3959353 77
## 529 Oklahoma OK 35.00775 -97.09288 3959353 77
## 530 Oklahoma OK 35.00775 -97.09288 3959353 77
## 531 Oklahoma OK 35.00775 -97.09288 3959353 77
## 532 Oklahoma OK 35.00775 -97.09288 3959353 77
## 533 Oklahoma OK 35.00775 -97.09288 3959353 77
## 534 Oklahoma OK 35.00775 -97.09288 3959353 77
## 535 Oklahoma OK 35.00775 -97.09288 3959353 77
## 536 Oregon OR 43.80413 -120.55420 4237256 36
## 537 Oregon OR 43.80413 -120.55420 4237256 36
## 538 Oregon OR 43.80413 -120.55420 4237256 36
## 539 Oregon OR 43.80413 -120.55420 4237256 36
## 540 Oregon OR 43.80413 -120.55420 4237256 36
## 541 Oregon OR 43.80413 -120.55420 4237256 36
## 542 Oregon OR 43.80413 -120.55420 4237256 36
## 543 Oregon OR 43.80413 -120.55420 4237256 36
## 544 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 545 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 546 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 547 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 548 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 549 Pennsylvania PA 41.20332 -77.19452 13002700 67
## 550 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 551 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 552 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 553 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 554 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 555 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 556 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 557 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 558 Puerto Rico PR 18.22083 -66.59015 3285874 78
## 559 Rhode Island RI 41.58010 -71.47743 1097379 5
## 560 Rhode Island RI 41.58010 -71.47743 1097379 5
## 561 Rhode Island RI 41.58010 -71.47743 1097379 5
## 562 Rhode Island RI 41.58010 -71.47743 1097379 5
## 563 Rhode Island RI 41.58010 -71.47743 1097379 5
## 564 South Carolina SC 33.83608 -81.16372 5118425 46
## 565 South Carolina SC 33.83608 -81.16372 5118425 46
## 566 South Carolina SC 33.83608 -81.16372 5118425 46
## 567 South Carolina SC 33.83608 -81.16372 5118425 46
## 568 South Carolina SC 33.83608 -81.16372 5118425 46
## 569 South Carolina SC 33.83608 -81.16372 5118425 46
## 570 South Carolina SC 33.83608 -81.16372 5118425 46
## 571 South Carolina SC 33.83608 -81.16372 5118425 46
## 572 South Dakota SD 43.96952 -99.90181 886667 66
## 573 South Dakota SD 43.96952 -99.90181 886667 66
## 574 South Dakota SD 43.96952 -99.90181 886667 66
## 575 South Dakota SD 43.96952 -99.90181 886667 66
## 576 South Dakota SD 43.96952 -99.90181 886667 66
## 577 South Dakota SD 43.96952 -99.90181 886667 66
## 578 South Dakota SD 43.96952 -99.90181 886667 66
## 579 South Dakota SD 43.96952 -99.90181 886667 66
## 580 South Dakota SD 43.96952 -99.90181 886667 66
## 581 South Dakota SD 43.96952 -99.90181 886667 66
## 582 Tennessee TN 35.51749 -86.58045 6910840 95
## 583 Tennessee TN 35.51749 -86.58045 6910840 95
## 584 Tennessee TN 35.51749 -86.58045 6910840 95
## 585 Tennessee TN 35.51749 -86.58045 6910840 95
## 586 Tennessee TN 35.51749 -86.58045 6910840 95
## 587 Tennessee TN 35.51749 -86.58045 6910840 95
## 588 Tennessee TN 35.51749 -86.58045 6910840 95
## 589 Texas TX 31.96860 -99.90181 29145505 254
## 590 Texas TX 31.96860 -99.90181 29145505 254
## 591 Texas TX 31.96860 -99.90181 29145505 254
## 592 Texas TX 31.96860 -99.90181 29145505 254
## 593 Texas TX 31.96860 -99.90181 29145505 254
## 594 Texas TX 31.96860 -99.90181 29145505 254
## 595 Texas TX 31.96860 -99.90181 29145505 254
## 596 Texas TX 31.96860 -99.90181 29145505 254
## 597 Texas TX 31.96860 -99.90181 29145505 254
## 598 Texas TX 31.96860 -99.90181 29145505 254
## 599 Texas TX 31.96860 -99.90181 29145505 254
## 600 Texas TX 31.96860 -99.90181 29145505 254
## 601 Texas TX 31.96860 -99.90181 29145505 254
## 602 Texas TX 31.96860 -99.90181 29145505 254
## 603 Texas TX 31.96860 -99.90181 29145505 254
## 604 Texas TX 31.96860 -99.90181 29145505 254
## 605 Utah UT 39.32098 -111.09373 3271616 29
## 606 Virginia VA 37.43157 -78.65689 8631393 135
## 607 Virginia VA 37.43157 -78.65689 8631393 135
## 608 Virginia VA 37.43157 -78.65689 8631393 135
## 609 Virginia VA 37.43157 -78.65689 8631393 135
## 610 Virginia VA 37.43157 -78.65689 8631393 135
## 611 Virginia VA 37.43157 -78.65689 8631393 135
## 612 Vermont VT 44.55880 -72.57784 643077 14
## 613 Vermont VT 44.55880 -72.57784 643077 14
## 614 Vermont VT 44.55880 -72.57784 643077 14
## 615 Vermont VT 44.55880 -72.57784 643077 14
## 616 Washington WA 47.75107 -120.74014 7705281 39
## 617 Washington WA 47.75107 -120.74014 7705281 39
## 618 Washington WA 47.75107 -120.74014 7705281 39
## 619 Washington WA 47.75107 -120.74014 7705281 39
## 620 Washington WA 47.75107 -120.74014 7705281 39
## 621 Washington WA 47.75107 -120.74014 7705281 39
## 622 Washington WA 47.75107 -120.74014 7705281 39
## 623 Washington WA 47.75107 -120.74014 7705281 39
## 624 Washington WA 47.75107 -120.74014 7705281 39
## 625 Washington WA 47.75107 -120.74014 7705281 39
## 626 Washington WA 47.75107 -120.74014 7705281 39
## 627 Wisconsin WI 43.78444 -88.78787 5893718 72
## 628 Wisconsin WI 43.78444 -88.78787 5893718 72
## 629 Wisconsin WI 43.78444 -88.78787 5893718 72
## 630 Wisconsin WI 43.78444 -88.78787 5893718 72
## 631 Wisconsin WI 43.78444 -88.78787 5893718 72
## 632 Wisconsin WI 43.78444 -88.78787 5893718 72
## 633 Wisconsin WI 43.78444 -88.78787 5893718 72
## 634 Wisconsin WI 43.78444 -88.78787 5893718 72
## 635 West Virginia WV 38.59763 -80.45490 1793716 55
## 636 West Virginia WV 38.59763 -80.45490 1793716 55
## 637 West Virginia WV 38.59763 -80.45490 1793716 55
## 638 West Virginia WV 38.59763 -80.45490 1793716 55
## 639 West Virginia WV 38.59763 -80.45490 1793716 55
## 640 West Virginia WV 38.59763 -80.45490 1793716 55
## 641 West Virginia WV 38.59763 -80.45490 1793716 55
## 642 West Virginia WV 38.59763 -80.45490 1793716 55
## 643 West Virginia WV 38.59763 -80.45490 1793716 55
## 644 West Virginia WV 38.59763 -80.45490 1793716 55
## 645 Wyoming WY 43.07597 -107.29028 576851 23
## 646 Wyoming WY 43.07597 -107.29028 576851 23
## FEMARegion disasterNumber declarationType
## 1 Region X 4122 DR
## 2 Region X 4533 DR
## 3 Region X 4413 DR
## 4 Region X 4094 DR
## 5 Region IV 4503 DR
## 6 Region IV 4362 DR
## 7 Region IV 4563 DR
## 8 Region IV 4176 DR
## 9 Region VI 4226 DR
## 10 Region VI 4174 DR
## 11 Region VI 4318 DR
## 12 Region VI 4254 DR
## 13 Region VI 4160 DR
## 14 Region VI 4518 DR
## 15 Region VI 4441 DR
## 16 Region VI 4270 DR
## 17 Region IX 3442 EM
## 18 Region IX 4524 DR
## 19 Region IX 4582 DR
## 20 Region IX 4482 DR
## 21 Region IX 3396 EM
## 22 Region IX 3381 EM
## 23 Region IX 4431 DR
## 24 Region IX 3428 EM
## 25 Region IX 4569 DR
## 26 Region IX 3415 EM
## 27 Region IX 4382 DR
## 28 Region IX 4353 DR
## 29 Region IX 4558 DR
## 30 Region IX 4407 DR
## 31 Region IX 4619 DR
## 32 Region IX 3398 EM
## 33 Region IX 4301 DR
## 34 Region IX 3409 EM
## 35 Region IX 4240 DR
## 36 Region IX 4344 DR
## 37 Region IX 4610 DR
## 38 Region IX 4308 DR
## 39 Region VIII 4229 DR
## 40 Region VIII 4067 DR
## 41 Region VIII 4498 DR
## 42 Region VIII 3365 EM
## 43 Region VIII 4145 DR
## 44 Region I 3535 EM
## 45 Region I 4500 DR
## 46 Region I 4087 DR
## 47 Region I 4580 DR
## 48 Region I 4106 DR
## 49 Region I 3353 EM
## 50 Region III 4502 DR
## 51 Region III 3553 EM
## 52 Region III 4526 DR
## 53 Region IV 3419 EM
## 54 Region IV 3560 EM
## 55 Region IV 3377 EM
## 56 Region IV 3405 EM
## 57 Region IV 4177 DR
## 58 Region IV 4283 DR
## 59 Region IV 4564 DR
## 60 Region IV 4068 DR
## 61 Region IV 4337 DR
## 62 Region IV 4341 DR
## 63 Region IV 4486 DR
## 64 Region IV 4399 DR
## 65 Region IV 3385 EM
## 66 Region IV 3422 EM
## 67 Region IV 4294 DR
## 68 Region IV 4501 DR
## 69 Region IV 4284 DR
## 70 Region IV 4297 DR
## 71 Region IV 4338 DR
## 72 Region IV 4400 DR
## 73 Region IV 3379 EM
## 74 Region IX 3399 EM
## 75 Region IX 4510 DR
## 76 Region IX 4366 DR
## 77 Region IX 4365 DR
## 78 Region IX 3529 EM
## 79 Region VII 4557 DR
## 80 Region VII 4126 DR
## 81 Region VII 4119 DR
## 82 Region VII 4421 DR
## 83 Region VII 4483 DR
## 84 Region X 4313 DR
## 85 Region X 4246 DR
## 86 Region X 4534 DR
## 87 Region X 4310 DR
## 88 Region V 4489 DR
## 89 Region V 4157 DR
## 90 Region V 4461 DR
## 91 Region V 4116 DR
## 92 Region V 4058 DR
## 93 Region V 4363 DR
## 94 Region V 4515 DR
## 95 Region VII 4504 DR
## 96 Region VII 3412 EM
## 97 Region VII 4063 DR
## 98 Region IV 4218 DR
## 99 Region IV 4497 DR
## 100 Region IV 4057 DR
## 101 Region IV 4358 DR
## 102 Region IV 4428 DR
## 103 Region VI 4570 DR
## 104 Region VI 3549 EM
## 105 Region VI 3416 EM
## 106 Region VI 4277 DR
## 107 Region VI 3538 EM
## 108 Region VI 4080 DR
## 109 Region VI 4611 DR
## 110 Region VI 3392 EM
## 111 Region VI 4559 DR
## 112 Region VI 3347 EM
## 113 Region VI 4577 DR
## 114 Region VI 4263 DR
## 115 Region VI 3543 EM
## 116 Region VI 3382 EM
## 117 Region VI 4484 DR
## 118 Region VI 3547 EM
## 119 Region I 3362 EM
## 120 Region I 4372 DR
## 121 Region I 3350 EM
## 122 Region I 4214 DR
## 123 Region I 4496 DR
## 124 Region I 4379 DR
## 125 Region I 4522 DR
## 126 Region I 4354 DR
## 127 Region V 4547 DR
## 128 Region V 4494 DR
## 129 Region V 4607 DR
## 130 Region V 4121 DR
## 131 Region V 4195 DR
## 132 Region V 4326 DR
## 133 Region V 3453 EM
## 134 Region V 4442 DR
## 135 Region V 4131 DR
## 136 Region V 4290 DR
## 137 Region V 4113 DR
## 138 Region V 4414 DR
## 139 Region V 4069 DR
## 140 Region V 4531 DR
## 141 Region V 4390 DR
## 142 Region V 4182 DR
## 143 Region VII 4317 DR
## 144 Region VII 4490 DR
## 145 Region VII 3374 EM
## 146 Region VII 4238 DR
## 147 Region VII 4250 DR
## 148 Region VII 4435 DR
## 149 Region VII 4451 DR
## 150 Region IV 4175 DR
## 151 Region IV 4081 DR
## 152 Region IV 4536 DR
## 153 Region IV 3569 EM
## 154 Region IV 4295 DR
## 155 Region IV 4268 DR
## 156 Region IV 4528 DR
## 157 Region IV 4248 DR
## 158 Region VIII 4127 DR
## 159 Region VIII 4508 DR
## 160 Region IV 4588 DR
## 161 Region IV 3380 EM
## 162 Region IV 3423 EM
## 163 Region IV 4465 DR
## 164 Region IV 4393 DR
## 165 Region IV 4487 DR
## 166 Region IV 4285 DR
## 167 Region IV 4146 DR
## 168 Region IV 4412 DR
## 169 Region VIII 4190 DR
## 170 Region VIII 4323 DR
## 171 Region VIII 3364 EM
## 172 Region VIII 4509 DR
## 173 Region VII 4446 DR
## 174 Region VII 4183 DR
## 175 Region VII 4521 DR
## 176 Region VII 4225 DR
## 177 Region VII 4420 DR
## 178 Region I 4370 DR
## 179 Region I 4105 DR
## 180 Region I 4355 DR
## 181 Region I 4516 DR
## 182 Region II 4086 DR
## 183 Region II 4488 DR
## 184 Region II 4614 DR
## 185 Region VI 4352 DR
## 186 Region VI 4151 DR
## 187 Region VI 4529 DR
## 188 Region IX 4523 DR
## 189 Region IX 4307 DR
## 190 Region IX 4303 DR
## 191 Region II 3565 EM
## 192 Region II 4480 DR
## 193 Region II 4085 DR
## 194 Region II 4129 DR
## 195 Region II 4615 DR
## 196 Region II 4180 DR
## 197 Region V 4507 DR
## 198 Region V 3346 EM
## 199 Region V 4077 DR
## 200 Region V 4447 DR
## 201 Region V 4360 DR
## 202 Region V 4098 DR
## 203 Region VI 4117 DR
## 204 Region VI 3502 EM
## 205 Region VI 4222 DR
## 206 Region VI 3494 EM
## 207 Region VI 4324 DR
## 208 Region VI 3486 EM
## 209 Region VI 4315 DR
## 210 Region VI 4530 DR
## 211 Region VI 4078 DR
## 212 Region VI 4438 DR
## 213 Region X 4432 DR
## 214 Region X 4452 DR
## 215 Region X 4296 DR
## 216 Region X 4328 DR
## 217 Region X 4562 DR
## 218 Region X 4055 DR
## 219 Region X 4499 DR
## 220 Region X 4258 DR
## 221 Region III 4149 DR
## 222 Region III 3356 EM
## 223 Region III 4618 DR
## 224 Region III 4506 DR
## 225 Region III 4408 DR
## 226 Region III 3367 EM
## 227 Region II 3426 EM
## 228 Region II 4571 DR
## 229 Region II 4473 DR
## 230 Region II 3532 EM
## 231 Region II 4493 DR
## 232 Region II 3417 EM
## 233 Region II 4339 DR
## 234 Region II 4336 DR
## 235 Region II 3391 EM
## 236 Region I 3563 EM
## 237 Region I 4107 DR
## 238 Region I 3355 EM
## 239 Region I 4089 DR
## 240 Region I 4505 DR
## 241 Region IV 3386 EM
## 242 Region IV 3373 EM
## 243 Region IV 4394 DR
## 244 Region IV 4241 DR
## 245 Region IV 3421 EM
## 246 Region IV 3378 EM
## 247 Region IV 4286 DR
## 248 Region IV 4492 DR
## 249 Region VIII 4237 DR
## 250 Region VIII 4527 DR
## 251 Region VIII 4155 DR
## 252 Region VIII 4125 DR
## 253 Region VIII 4115 DR
## 254 Region VIII 4233 DR
## 255 Region VIII 4186 DR
## 256 Region VIII 4298 DR
## 257 Region VIII 3536 EM
## 258 Region VIII 4440 DR
## 259 Region IV 4293 DR
## 260 Region IV 4476 DR
## 261 Region IV 4514 DR
## 262 Region IV 4211 DR
## 263 Region IV 4060 DR
## 264 Region IV 4609 DR
## 265 Region IV 4189 DR
## 266 Region VI 4586 DR
## 267 Region VI 4223 DR
## 268 Region VI 4159 DR
## 269 Region VI 4269 DR
## 270 Region VI 4272 DR
## 271 Region VI 4416 DR
## 272 Region VI 4572 DR
## 273 Region VI 3530 EM
## 274 Region VI 4266 DR
## 275 Region VI 4332 DR
## 276 Region VI 4136 DR
## 277 Region VI 4377 DR
## 278 Region VI 4245 DR
## 279 Region VI 4255 DR
## 280 Region VI 4485 DR
## 281 Region VI 3363 EM
## 282 Region VIII 4525 DR
## 283 Region III 4291 DR
## 284 Region III 4401 DR
## 285 Region III 4092 DR
## 286 Region III 4512 DR
## 287 Region III 4072 DR
## 288 Region III 3403 EM
## 289 Region I 4356 DR
## 290 Region I 3437 EM
## 291 Region I 4532 DR
## 292 Region I 4445 DR
## 293 Region X 4593 DR
## 294 Region X 4309 DR
## 295 Region X 4481 DR
## 296 Region X 4249 DR
## 297 Region X 4243 DR
## 298 Region X 4056 DR
## 299 Region X 3371 EM
## 300 Region X 3372 EM
## 301 Region X 3370 EM
## 302 Region X 4188 DR
## 303 Region X 4168 DR
## 304 Region V 4076 DR
## 305 Region V 4402 DR
## 306 Region V 4288 DR
## 307 Region V 4520 DR
## 308 Region V 4141 DR
## 309 Region V 4459 DR
## 310 Region V 4383 DR
## 311 Region V 4276 DR
## 312 Region III 3358 EM
## 313 Region III 4273 DR
## 314 Region III 4221 DR
## 315 Region III 4359 DR
## 316 Region III 4093 DR
## 317 Region III 4061 DR
## 318 Region III 4517 DR
## 319 Region III 4071 DR
## 320 Region III 4059 DR
## 321 Region III 4210 DR
## 322 Region VIII 4535 DR
## 323 Region VIII 4227 DR
## 324 Region X 4122 DR
## 325 Region X 4533 DR
## 326 Region X 4413 DR
## 327 Region X 4094 DR
## 328 Region IV 4503 DR
## 329 Region IV 4362 DR
## 330 Region IV 4563 DR
## 331 Region IV 4176 DR
## 332 Region VI 4226 DR
## 333 Region VI 4174 DR
## 334 Region VI 4318 DR
## 335 Region VI 4254 DR
## 336 Region VI 4160 DR
## 337 Region VI 4518 DR
## 338 Region VI 4441 DR
## 339 Region VI 4270 DR
## 340 Region IX 3442 EM
## 341 Region IX 4524 DR
## 342 Region IX 4582 DR
## 343 Region IX 4482 DR
## 344 Region IX 3396 EM
## 345 Region IX 3381 EM
## 346 Region IX 4431 DR
## 347 Region IX 3428 EM
## 348 Region IX 4569 DR
## 349 Region IX 3415 EM
## 350 Region IX 4382 DR
## 351 Region IX 4353 DR
## 352 Region IX 4558 DR
## 353 Region IX 4407 DR
## 354 Region IX 4619 DR
## 355 Region IX 3398 EM
## 356 Region IX 4301 DR
## 357 Region IX 3409 EM
## 358 Region IX 4240 DR
## 359 Region IX 4344 DR
## 360 Region IX 4610 DR
## 361 Region IX 4308 DR
## 362 Region VIII 4229 DR
## 363 Region VIII 4067 DR
## 364 Region VIII 4498 DR
## 365 Region VIII 3365 EM
## 366 Region VIII 4145 DR
## 367 Region I 3535 EM
## 368 Region I 4500 DR
## 369 Region I 4087 DR
## 370 Region I 4580 DR
## 371 Region I 4106 DR
## 372 Region I 3353 EM
## 373 Region III 4502 DR
## 374 Region III 3553 EM
## 375 Region III 4526 DR
## 376 Region IV 3419 EM
## 377 Region IV 3560 EM
## 378 Region IV 3377 EM
## 379 Region IV 3405 EM
## 380 Region IV 4177 DR
## 381 Region IV 4283 DR
## 382 Region IV 4564 DR
## 383 Region IV 4068 DR
## 384 Region IV 4337 DR
## 385 Region IV 4341 DR
## 386 Region IV 4486 DR
## 387 Region IV 4399 DR
## 388 Region IV 3385 EM
## 389 Region IV 3422 EM
## 390 Region IV 4294 DR
## 391 Region IV 4501 DR
## 392 Region IV 4284 DR
## 393 Region IV 4297 DR
## 394 Region IV 4338 DR
## 395 Region IV 4400 DR
## 396 Region IV 3379 EM
## 397 Region IX 3399 EM
## 398 Region IX 4510 DR
## 399 Region IX 4366 DR
## 400 Region IX 4365 DR
## 401 Region IX 3529 EM
## 402 Region VII 4557 DR
## 403 Region VII 4126 DR
## 404 Region VII 4119 DR
## 405 Region VII 4421 DR
## 406 Region VII 4483 DR
## 407 Region X 4313 DR
## 408 Region X 4246 DR
## 409 Region X 4534 DR
## 410 Region X 4310 DR
## 411 Region V 4489 DR
## 412 Region V 4157 DR
## 413 Region V 4461 DR
## 414 Region V 4116 DR
## 415 Region V 4058 DR
## 416 Region V 4363 DR
## 417 Region V 4515 DR
## 418 Region VII 4504 DR
## 419 Region VII 3412 EM
## 420 Region VII 4063 DR
## 421 Region IV 4218 DR
## 422 Region IV 4497 DR
## 423 Region IV 4057 DR
## 424 Region IV 4358 DR
## 425 Region IV 4428 DR
## 426 Region VI 4570 DR
## 427 Region VI 3549 EM
## 428 Region VI 3416 EM
## 429 Region VI 4277 DR
## 430 Region VI 3538 EM
## 431 Region VI 4080 DR
## 432 Region VI 4611 DR
## 433 Region VI 3392 EM
## 434 Region VI 4559 DR
## 435 Region VI 3347 EM
## 436 Region VI 4577 DR
## 437 Region VI 4263 DR
## 438 Region VI 3543 EM
## 439 Region VI 3382 EM
## 440 Region VI 4484 DR
## 441 Region VI 3547 EM
## 442 Region I 3362 EM
## 443 Region I 4372 DR
## 444 Region I 3350 EM
## 445 Region I 4214 DR
## 446 Region I 4496 DR
## 447 Region I 4379 DR
## 448 Region I 4522 DR
## 449 Region I 4354 DR
## 450 Region V 4547 DR
## 451 Region V 4494 DR
## 452 Region V 4607 DR
## 453 Region V 4121 DR
## 454 Region V 4195 DR
## 455 Region V 4326 DR
## 456 Region V 3453 EM
## 457 Region V 4442 DR
## 458 Region V 4131 DR
## 459 Region V 4290 DR
## 460 Region V 4113 DR
## 461 Region V 4414 DR
## 462 Region V 4069 DR
## 463 Region V 4531 DR
## 464 Region V 4390 DR
## 465 Region V 4182 DR
## 466 Region VII 4317 DR
## 467 Region VII 4490 DR
## 468 Region VII 3374 EM
## 469 Region VII 4238 DR
## 470 Region VII 4250 DR
## 471 Region VII 4435 DR
## 472 Region VII 4451 DR
## 473 Region IV 4175 DR
## 474 Region IV 4081 DR
## 475 Region IV 4536 DR
## 476 Region IV 3569 EM
## 477 Region IV 4295 DR
## 478 Region IV 4268 DR
## 479 Region IV 4528 DR
## 480 Region IV 4248 DR
## 481 Region VIII 4127 DR
## 482 Region VIII 4508 DR
## 483 Region IV 4588 DR
## 484 Region IV 3380 EM
## 485 Region IV 3423 EM
## 486 Region IV 4465 DR
## 487 Region IV 4393 DR
## 488 Region IV 4487 DR
## 489 Region IV 4285 DR
## 490 Region IV 4146 DR
## 491 Region IV 4412 DR
## 492 Region VIII 4190 DR
## 493 Region VIII 4323 DR
## 494 Region VIII 3364 EM
## 495 Region VIII 4509 DR
## 496 Region VII 4446 DR
## 497 Region VII 4183 DR
## 498 Region VII 4521 DR
## 499 Region VII 4225 DR
## 500 Region VII 4420 DR
## 501 Region I 4370 DR
## 502 Region I 4105 DR
## 503 Region I 4355 DR
## 504 Region I 4516 DR
## 505 Region II 4086 DR
## 506 Region II 4488 DR
## 507 Region II 4614 DR
## 508 Region VI 4352 DR
## 509 Region VI 4151 DR
## 510 Region VI 4529 DR
## 511 Region IX 4523 DR
## 512 Region IX 4307 DR
## 513 Region IX 4303 DR
## 514 Region II 3565 EM
## 515 Region II 4480 DR
## 516 Region II 4085 DR
## 517 Region II 4129 DR
## 518 Region II 4615 DR
## 519 Region II 4180 DR
## 520 Region V 4507 DR
## 521 Region V 3346 EM
## 522 Region V 4077 DR
## 523 Region V 4447 DR
## 524 Region V 4360 DR
## 525 Region V 4098 DR
## 526 Region VI 4117 DR
## 527 Region VI 3502 EM
## 528 Region VI 4222 DR
## 529 Region VI 3494 EM
## 530 Region VI 4324 DR
## 531 Region VI 3486 EM
## 532 Region VI 4315 DR
## 533 Region VI 4530 DR
## 534 Region VI 4078 DR
## 535 Region VI 4438 DR
## 536 Region X 4432 DR
## 537 Region X 4452 DR
## 538 Region X 4296 DR
## 539 Region X 4328 DR
## 540 Region X 4562 DR
## 541 Region X 4055 DR
## 542 Region X 4499 DR
## 543 Region X 4258 DR
## 544 Region III 4149 DR
## 545 Region III 3356 EM
## 546 Region III 4618 DR
## 547 Region III 4506 DR
## 548 Region III 4408 DR
## 549 Region III 3367 EM
## 550 Region II 3426 EM
## 551 Region II 4571 DR
## 552 Region II 4473 DR
## 553 Region II 3532 EM
## 554 Region II 4493 DR
## 555 Region II 3417 EM
## 556 Region II 4339 DR
## 557 Region II 4336 DR
## 558 Region II 3391 EM
## 559 Region I 3563 EM
## 560 Region I 4107 DR
## 561 Region I 3355 EM
## 562 Region I 4089 DR
## 563 Region I 4505 DR
## 564 Region IV 3386 EM
## 565 Region IV 3373 EM
## 566 Region IV 4394 DR
## 567 Region IV 4241 DR
## 568 Region IV 3421 EM
## 569 Region IV 3378 EM
## 570 Region IV 4286 DR
## 571 Region IV 4492 DR
## 572 Region VIII 4237 DR
## 573 Region VIII 4527 DR
## 574 Region VIII 4155 DR
## 575 Region VIII 4125 DR
## 576 Region VIII 4115 DR
## 577 Region VIII 4233 DR
## 578 Region VIII 4186 DR
## 579 Region VIII 4298 DR
## 580 Region VIII 3536 EM
## 581 Region VIII 4440 DR
## 582 Region IV 4293 DR
## 583 Region IV 4476 DR
## 584 Region IV 4514 DR
## 585 Region IV 4211 DR
## 586 Region IV 4060 DR
## 587 Region IV 4609 DR
## 588 Region IV 4189 DR
## 589 Region VI 4586 DR
## 590 Region VI 4223 DR
## 591 Region VI 4159 DR
## 592 Region VI 4269 DR
## 593 Region VI 4272 DR
## 594 Region VI 4416 DR
## 595 Region VI 4572 DR
## 596 Region VI 3530 EM
## 597 Region VI 4266 DR
## 598 Region VI 4332 DR
## 599 Region VI 4136 DR
## 600 Region VI 4377 DR
## 601 Region VI 4245 DR
## 602 Region VI 4255 DR
## 603 Region VI 4485 DR
## 604 Region VI 3363 EM
## 605 Region VIII 4525 DR
## 606 Region III 4291 DR
## 607 Region III 4401 DR
## 608 Region III 4092 DR
## 609 Region III 4512 DR
## 610 Region III 4072 DR
## 611 Region III 3403 EM
## 612 Region I 4356 DR
## 613 Region I 3437 EM
## 614 Region I 4532 DR
## 615 Region I 4445 DR
## 616 Region X 4593 DR
## 617 Region X 4309 DR
## 618 Region X 4481 DR
## 619 Region X 4249 DR
## 620 Region X 4243 DR
## 621 Region X 4056 DR
## 622 Region X 3371 EM
## 623 Region X 3372 EM
## 624 Region X 3370 EM
## 625 Region X 4188 DR
## 626 Region X 4168 DR
## 627 Region V 4076 DR
## 628 Region V 4402 DR
## 629 Region V 4288 DR
## 630 Region V 4520 DR
## 631 Region V 4141 DR
## 632 Region V 4459 DR
## 633 Region V 4383 DR
## 634 Region V 4276 DR
## 635 Region III 3358 EM
## 636 Region III 4273 DR
## 637 Region III 4221 DR
## 638 Region III 4359 DR
## 639 Region III 4093 DR
## 640 Region III 4061 DR
## 641 Region III 4517 DR
## 642 Region III 4071 DR
## 643 Region III 4059 DR
## 644 Region III 4210 DR
## 645 Region VIII 4535 DR
## 646 Region VIII 4227 DR
## declarationTitle
## 1 FLOODING
## 2 COVID-19 PANDEMIC
## 3 EARTHQUAKE
## 4 SEVERE STORM, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 5 COVID-19 PANDEMIC
## 6 SEVERE STORMS AND TORNADOES
## 7 HURRICANE SALLY
## 8 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 9 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 10 SEVERE STORMS,TORNADOES, AND FLOODING
## 11 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 12 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 13 SEVERE WINTER STORM
## 14 COVID-19 PANDEMIC
## 15 SEVERE STORMS AND FLOODING
## 16 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 17 COVID-19
## 18 COVID-19 PANDEMIC
## 19 COVID-19 PANDEMIC
## 20 COVID-19 PANDEMIC
## 21 WILDFIRES
## 22 POTENTIAL FAILURE OF THE EMERGENCY SPILLWAY AT OROVILLE LAKE
## 23 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 24 COVID-19
## 25 WILDFIRES
## 26 EARTHQUAKES
## 27 WILDFIRES AND HIGH WINDS
## 28 WILDFIRES, FLOODING, MUDFLOWS, AND DEBRIS FLOWS
## 29 WILDFIRES
## 30 WILDFIRES
## 31 CALDOR FIRE
## 32 WILDFIRE
## 33 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 34 WILDFIRES
## 35 VALLEY FIRE AND BUTTE FIRE
## 36 WILDFIRES
## 37 WILDFIRES
## 38 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 39 SEVERE STORMS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 40 HIGH PARK AND WALDO CANYON WILDFIRES
## 41 COVID-19 PANDEMIC
## 42 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 43 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 44 TROPICAL STORM ISAIAS
## 45 COVID-19 PANDEMIC
## 46 HURRICANE SANDY
## 47 TROPICAL STORM ISAIAS
## 48 SEVERE WINTER STORM AND SNOWSTORM
## 49 HURRICANE SANDY
## 50 COVID-19 PANDEMIC
## 51 59TH PRESIDENTIAL INAUGURATION
## 52 COVID-19 PANDEMIC
## 53 HURRICANE DORIAN
## 54 SURFSIDE BUILDING COLLAPSE
## 55 HURRICANE MATTHEW
## 56 HURRICANE MICHAEL
## 57 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 58 HURRICANE MATTHEW
## 59 HURRICANE SALLY
## 60 TROPICAL STORM DEBBY
## 61 HURRICANE IRMA
## 62 HURRICANE IRMA - SEMINOLE TRIBE OF FLORIDA
## 63 COVID-19 PANDEMIC
## 64 HURRICANE MICHAEL
## 65 HURRICANE IRMA
## 66 HURRICANE DORIAN
## 67 SEVERE STORMS, TORNADOES, AND STRAIGHT-LINE WINDS
## 68 COVID-19 PANDEMIC
## 69 HURRICANE MATTHEW
## 70 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 71 HURRICANE IRMA
## 72 HURRICANE MICHAEL
## 73 HURRICANE MATTHEW
## 74 HURRICANE LANE
## 75 COVID-19 PANDEMIC
## 76 KILAUEA VOLCANIC ERUPTION AND EARTHQUAKES
## 77 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 78 HURRICANE DOUGLAS
## 79 SEVERE STORMS
## 80 SEVERE STORMS, TORNADOES, AND FLOODING
## 81 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 82 SEVERE STORMS AND FLOODING
## 83 COVID-19 PANDEMIC
## 84 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 85 SEVERE STORM AND STRAIGHT-LINE WIND
## 86 COVID-19 PANDEMIC
## 87 SEVERE WINTER STORMS AND FLOODING
## 88 COVID-19 PANDEMIC
## 89 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 90 SEVERE STORMS AND FLOODING
## 91 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 92 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 93 SEVERE STORMS AND FLOODING
## 94 COVID-19 PANDEMIC
## 95 COVID-19 PANDEMIC
## 96 TORNADOES AND FLOODING
## 97 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 98 SEVERE WINTER STORM, SNOWSTORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 99 COVID-19 PANDEMIC
## 100 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 101 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 102 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 103 HURRICANE DELTA
## 104 TROPICAL STORM ZETA
## 105 TROPICAL STORM BARRY
## 106 SEVERE STORMS AND FLOODING
## 107 TROPICAL STORMS LAURA AND MARCO
## 108 HURRICANE ISAAC
## 109 HURRICANE IDA
## 110 TROPICAL STORM NATE
## 111 HURRICANE LAURA
## 112 TROPICAL STORM ISAAC
## 113 HURRICANE ZETA
## 114 SEVERE STORMS AND FLOODING
## 115 HURRICANE SALLY
## 116 TROPICAL STORM HARVEY
## 117 COVID-19 PANDEMIC
## 118 HURRICANE DELTA
## 119 EXPLOSIONS
## 120 SEVERE WINTER STORM AND FLOODING
## 121 HURRICANE SANDY
## 122 SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 123 COVID-19 PANDEMIC
## 124 SEVERE WINTER STORM AND SNOWSTORM
## 125 COVID-19 PANDEMIC
## 126 SEVERE STORM AND FLOODING
## 127 SEVERE STORMS AND FLOODING
## 128 COVID-19 PANDEMIC
## 129 SEVERE STORMS, FLOODING, AND TORNADOES
## 130 FLOODING
## 131 SEVERE STORMS AND FLOODING
## 132 SEVERE STORMS AND FLOODING
## 133 COVID-19
## 134 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 135 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 136 SEVERE STORMS AND FLOODING
## 137 SEVERE WINTER STORM
## 138 SEVERE STORMS AND FLOODING
## 139 SEVERE STORMS AND FLOODING
## 140 COVID-19 PANDEMIC
## 141 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 142 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 143 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 144 COVID-19 PANDEMIC
## 145 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 146 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 147 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 148 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 149 SEVERE STORMS, TORNADOES, AND FLOODING
## 150 SEVERE STORMS, TORNADOES, AND FLOODING
## 151 HURRICANE ISAAC
## 152 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 153 HURRICANE IDA
## 154 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 155 SEVERE STORMS AND FLOODING
## 156 COVID-19 PANDEMIC
## 157 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 158 FLOODING
## 159 COVID-19 PANDEMIC
## 160 TROPICAL STORM ETA
## 161 HURRICANE MATTHEW
## 162 HURRICANE DORIAN
## 163 HURRICANE DORIAN
## 164 HURRICANE FLORENCE
## 165 COVID-19 PANDEMIC
## 166 HURRICANE MATTHEW
## 167 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 168 TROPICAL STORM MICHAEL
## 169 SEVERE STORMS AND FLOODING
## 170 FLOODING
## 171 FLOODING
## 172 COVID-19 PANDEMIC
## 173 SEVERE STORMS AND FLOODING
## 174 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 175 COVID-19 PANDEMIC
## 176 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 177 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 178 SEVERE STORM AND FLOODING
## 179 SEVERE WINTER STORM AND SNOWSTORM
## 180 SEVERE STORM AND FLOODING
## 181 COVID-19 PANDEMIC
## 182 HURRICANE SANDY
## 183 COVID-19 PANDEMIC
## 184 REMNANTS OF HURRICANE IDA
## 185 SEVERE STORMS AND FLOODING
## 186 SEVERE STORMS AND FLOODING
## 187 COVID-19 PANDEMIC
## 188 COVID-19 PANDEMIC
## 189 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 190 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 191 HURRICANE HENRI
## 192 COVID-19 PANDEMIC
## 193 HURRICANE SANDY
## 194 SEVERE STORMS AND FLOODING
## 195 REMNANTS OF HURRICANE IDA
## 196 SEVERE STORMS AND FLOODING
## 197 COVID-19 PANDEMIC
## 198 SEVERE STORMS
## 199 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 200 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDE
## 201 SEVERE STORMS, LANDSLIDES, AND MUDSLIDES
## 202 SEVERE STORMS AND FLOODING DUE TO THE REMNANTS OF HURRICANE SANDY
## 203 SEVERE STORMS, TORNADOES, AND FLOODING
## 204 COVID-19
## 205 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 206 COVID-19
## 207 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 208 COVID-19
## 209 SEVERE STORMS, TORNADOES, AND FLOODING
## 210 COVID-19 PANDEMIC
## 211 FREEDOM AND NOBLE WILDFIRES
## 212 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 213 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 214 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 215 SEVERE WINTER STORM AND FLOODING
## 216 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 217 WILDFIRES AND STRAIGHT-LINE WINDS
## 218 SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 219 COVID-19 PANDEMIC
## 220 SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 221 SEVERE STORMS, TORNADOES, AND FLOODING
## 222 HURRICANE SANDY
## 223 REMNANTS OF HURRICANE IDA
## 224 COVID-19 PANDEMIC
## 225 SEVERE STORMS AND FLOODING
## 226 SEVERE WINTER STORM
## 227 EARTHQUAKES
## 228 SEVERE STORM AND FLOODING
## 229 EARTHQUAKES
## 230 POTENTIAL TROPICAL CYCLONE NINE
## 231 COVID-19 PANDEMIC
## 232 TROPICAL STORM DORIAN
## 233 HURRICANE MARIA
## 234 HURRICANE IRMA
## 235 HURRICANE MARIA
## 236 HURRICANE HENRI
## 237 SEVERE WINTER STORM AND SNOWSTORM
## 238 HURRICANE SANDY
## 239 HURRICANE SANDY
## 240 COVID-19 PANDEMIC
## 241 HURRICANE IRMA
## 242 SEVERE STORMS AND FLOODING
## 243 HURRICANE FLORENCE
## 244 SEVERE STORMS AND FLOODING
## 245 HURRICANE DORIAN
## 246 HURRICANE MATTHEW
## 247 HURRICANE MATTHEW
## 248 COVID-19 PANDEMIC
## 249 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 250 COVID-19 PANDEMIC
## 251 SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 252 SEVERE STORMS, TORNADO, AND FLOODING
## 253 SEVERE WINTER STORM AND SNOWSTORM
## 254 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 255 SEVERE STORMS, TORNADOES, AND FLOODING
## 256 SEVERE WINTER STORM
## 257 COVID-19
## 258 SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 259 WILDFIRES
## 260 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 261 COVID-19 PANDEMIC
## 262 SEVERE WINTER STORM AND FLOODING
## 263 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND
## 264 SEVERE STORM AND FLOODING
## 265 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 266 SEVERE WINTER STORMS
## 267 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 268 SEVERE STORMS AND FLOODING
## 269 SEVERE STORMS AND FLOODING
## 270 SEVERE STORMS AND FLOODING
## 271 SEVERE STORMS AND FLOODING
## 272 HURRICANE LAURA
## 273 HURRICANE HANNA
## 274 SEVERE STORMS, TORNADOES, AND FLOODING
## 275 HURRICANE HARVEY
## 276 EXPLOSION
## 277 SEVERE STORMS AND FLOODING
## 278 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 279 SEVERE WINTER STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 280 COVID-19 PANDEMIC
## 281 EXPLOSION
## 282 COVID-19 PANDEMIC
## 283 HURRICANE MATTHEW
## 284 HURRICANE FLORENCE
## 285 HURRICANE SANDY
## 286 COVID-19 PANDEMIC
## 287 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 288 HURRICANE FLORENCE
## 289 SEVERE STORM AND FLOODING
## 290 COVID-19
## 291 COVID-19 PANDEMIC
## 292 SEVERE STORMS AND FLOODING
## 293 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 294 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, MUDSLIDES
## 295 COVID-19 PANDEMIC
## 296 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 297 WILDFIRES AND MUDSLIDES
## 298 SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 299 WILDFIRES
## 300 WILDFIRES
## 301 FLOODING AND MUDSLIDES
## 302 WILDFIRES
## 303 FLOODING AND MUDSLIDES
## 304 SEVERE STORMS AND FLOODING
## 305 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 306 SEVERE STORMS, FLOODING, AND MUDSLIDES
## 307 COVID-19 PANDEMIC
## 308 SEVERE STORMS, FLOODING, AND MUDSLIDES
## 309 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 310 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 311 SEVERE STORMS AND FLOODING
## 312 HURRICANE SANDY
## 313 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 314 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 315 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 316 HURRICANE SANDY
## 317 SEVERE STORMS, FLOODING, MUDSLIDES, AND LANSLIDES
## 318 COVID-19 PANDEMIC
## 319 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 320 SEVERE STORMS, TORNADOES, FLOODING, MUDSLIDES, AND LANDSLIDES
## 321 SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 322 COVID-19 PANDEMIC
## 323 SEVERE STORMS AND FLOODING
## 324 FLOODING
## 325 COVID-19 PANDEMIC
## 326 EARTHQUAKE
## 327 SEVERE STORM, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 328 COVID-19 PANDEMIC
## 329 SEVERE STORMS AND TORNADOES
## 330 HURRICANE SALLY
## 331 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 332 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 333 SEVERE STORMS,TORNADOES, AND FLOODING
## 334 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 335 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 336 SEVERE WINTER STORM
## 337 COVID-19 PANDEMIC
## 338 SEVERE STORMS AND FLOODING
## 339 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 340 COVID-19
## 341 COVID-19 PANDEMIC
## 342 COVID-19 PANDEMIC
## 343 COVID-19 PANDEMIC
## 344 WILDFIRES
## 345 POTENTIAL FAILURE OF THE EMERGENCY SPILLWAY AT OROVILLE LAKE
## 346 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 347 COVID-19
## 348 WILDFIRES
## 349 EARTHQUAKES
## 350 WILDFIRES AND HIGH WINDS
## 351 WILDFIRES, FLOODING, MUDFLOWS, AND DEBRIS FLOWS
## 352 WILDFIRES
## 353 WILDFIRES
## 354 CALDOR FIRE
## 355 WILDFIRE
## 356 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 357 WILDFIRES
## 358 VALLEY FIRE AND BUTTE FIRE
## 359 WILDFIRES
## 360 WILDFIRES
## 361 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 362 SEVERE STORMS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDES
## 363 HIGH PARK AND WALDO CANYON WILDFIRES
## 364 COVID-19 PANDEMIC
## 365 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 366 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 367 TROPICAL STORM ISAIAS
## 368 COVID-19 PANDEMIC
## 369 HURRICANE SANDY
## 370 TROPICAL STORM ISAIAS
## 371 SEVERE WINTER STORM AND SNOWSTORM
## 372 HURRICANE SANDY
## 373 COVID-19 PANDEMIC
## 374 59TH PRESIDENTIAL INAUGURATION
## 375 COVID-19 PANDEMIC
## 376 HURRICANE DORIAN
## 377 SURFSIDE BUILDING COLLAPSE
## 378 HURRICANE MATTHEW
## 379 HURRICANE MICHAEL
## 380 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 381 HURRICANE MATTHEW
## 382 HURRICANE SALLY
## 383 TROPICAL STORM DEBBY
## 384 HURRICANE IRMA
## 385 HURRICANE IRMA - SEMINOLE TRIBE OF FLORIDA
## 386 COVID-19 PANDEMIC
## 387 HURRICANE MICHAEL
## 388 HURRICANE IRMA
## 389 HURRICANE DORIAN
## 390 SEVERE STORMS, TORNADOES, AND STRAIGHT-LINE WINDS
## 391 COVID-19 PANDEMIC
## 392 HURRICANE MATTHEW
## 393 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 394 HURRICANE IRMA
## 395 HURRICANE MICHAEL
## 396 HURRICANE MATTHEW
## 397 HURRICANE LANE
## 398 COVID-19 PANDEMIC
## 399 KILAUEA VOLCANIC ERUPTION AND EARTHQUAKES
## 400 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 401 HURRICANE DOUGLAS
## 402 SEVERE STORMS
## 403 SEVERE STORMS, TORNADOES, AND FLOODING
## 404 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 405 SEVERE STORMS AND FLOODING
## 406 COVID-19 PANDEMIC
## 407 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 408 SEVERE STORM AND STRAIGHT-LINE WIND
## 409 COVID-19 PANDEMIC
## 410 SEVERE WINTER STORMS AND FLOODING
## 411 COVID-19 PANDEMIC
## 412 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 413 SEVERE STORMS AND FLOODING
## 414 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 415 SEVERE STORMS, STRAIGHT-LINE WINDS, AND TORNADOES
## 416 SEVERE STORMS AND FLOODING
## 417 COVID-19 PANDEMIC
## 418 COVID-19 PANDEMIC
## 419 TORNADOES AND FLOODING
## 420 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 421 SEVERE WINTER STORM, SNOWSTORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 422 COVID-19 PANDEMIC
## 423 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 424 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 425 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 426 HURRICANE DELTA
## 427 TROPICAL STORM ZETA
## 428 TROPICAL STORM BARRY
## 429 SEVERE STORMS AND FLOODING
## 430 TROPICAL STORMS LAURA AND MARCO
## 431 HURRICANE ISAAC
## 432 HURRICANE IDA
## 433 TROPICAL STORM NATE
## 434 HURRICANE LAURA
## 435 TROPICAL STORM ISAAC
## 436 HURRICANE ZETA
## 437 SEVERE STORMS AND FLOODING
## 438 HURRICANE SALLY
## 439 TROPICAL STORM HARVEY
## 440 COVID-19 PANDEMIC
## 441 HURRICANE DELTA
## 442 EXPLOSIONS
## 443 SEVERE WINTER STORM AND FLOODING
## 444 HURRICANE SANDY
## 445 SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 446 COVID-19 PANDEMIC
## 447 SEVERE WINTER STORM AND SNOWSTORM
## 448 COVID-19 PANDEMIC
## 449 SEVERE STORM AND FLOODING
## 450 SEVERE STORMS AND FLOODING
## 451 COVID-19 PANDEMIC
## 452 SEVERE STORMS, FLOODING, AND TORNADOES
## 453 FLOODING
## 454 SEVERE STORMS AND FLOODING
## 455 SEVERE STORMS AND FLOODING
## 456 COVID-19
## 457 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 458 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 459 SEVERE STORMS AND FLOODING
## 460 SEVERE WINTER STORM
## 461 SEVERE STORMS AND FLOODING
## 462 SEVERE STORMS AND FLOODING
## 463 COVID-19 PANDEMIC
## 464 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 465 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 466 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 467 COVID-19 PANDEMIC
## 468 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 469 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 470 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 471 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 472 SEVERE STORMS, TORNADOES, AND FLOODING
## 473 SEVERE STORMS, TORNADOES, AND FLOODING
## 474 HURRICANE ISAAC
## 475 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 476 HURRICANE IDA
## 477 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 478 SEVERE STORMS AND FLOODING
## 479 COVID-19 PANDEMIC
## 480 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 481 FLOODING
## 482 COVID-19 PANDEMIC
## 483 TROPICAL STORM ETA
## 484 HURRICANE MATTHEW
## 485 HURRICANE DORIAN
## 486 HURRICANE DORIAN
## 487 HURRICANE FLORENCE
## 488 COVID-19 PANDEMIC
## 489 HURRICANE MATTHEW
## 490 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 491 TROPICAL STORM MICHAEL
## 492 SEVERE STORMS AND FLOODING
## 493 FLOODING
## 494 FLOODING
## 495 COVID-19 PANDEMIC
## 496 SEVERE STORMS AND FLOODING
## 497 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 498 COVID-19 PANDEMIC
## 499 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 500 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, AND FLOODING
## 501 SEVERE STORM AND FLOODING
## 502 SEVERE WINTER STORM AND SNOWSTORM
## 503 SEVERE STORM AND FLOODING
## 504 COVID-19 PANDEMIC
## 505 HURRICANE SANDY
## 506 COVID-19 PANDEMIC
## 507 REMNANTS OF HURRICANE IDA
## 508 SEVERE STORMS AND FLOODING
## 509 SEVERE STORMS AND FLOODING
## 510 COVID-19 PANDEMIC
## 511 COVID-19 PANDEMIC
## 512 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 513 SEVERE WINTER STORMS, FLOODING, AND MUDSLIDES
## 514 HURRICANE HENRI
## 515 COVID-19 PANDEMIC
## 516 HURRICANE SANDY
## 517 SEVERE STORMS AND FLOODING
## 518 REMNANTS OF HURRICANE IDA
## 519 SEVERE STORMS AND FLOODING
## 520 COVID-19 PANDEMIC
## 521 SEVERE STORMS
## 522 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 523 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, FLOODING, LANDSLIDES, AND MUDSLIDE
## 524 SEVERE STORMS, LANDSLIDES, AND MUDSLIDES
## 525 SEVERE STORMS AND FLOODING DUE TO THE REMNANTS OF HURRICANE SANDY
## 526 SEVERE STORMS, TORNADOES, AND FLOODING
## 527 COVID-19
## 528 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 529 COVID-19
## 530 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 531 COVID-19
## 532 SEVERE STORMS, TORNADOES, AND FLOODING
## 533 COVID-19 PANDEMIC
## 534 FREEDOM AND NOBLE WILDFIRES
## 535 SEVERE STORMS, STRAIGHT-LINE WINDS, TORNADOES, AND FLOODING
## 536 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 537 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 538 SEVERE WINTER STORM AND FLOODING
## 539 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 540 WILDFIRES AND STRAIGHT-LINE WINDS
## 541 SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 542 COVID-19 PANDEMIC
## 543 SEVERE WINTER STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 544 SEVERE STORMS, TORNADOES, AND FLOODING
## 545 HURRICANE SANDY
## 546 REMNANTS OF HURRICANE IDA
## 547 COVID-19 PANDEMIC
## 548 SEVERE STORMS AND FLOODING
## 549 SEVERE WINTER STORM
## 550 EARTHQUAKES
## 551 SEVERE STORM AND FLOODING
## 552 EARTHQUAKES
## 553 POTENTIAL TROPICAL CYCLONE NINE
## 554 COVID-19 PANDEMIC
## 555 TROPICAL STORM DORIAN
## 556 HURRICANE MARIA
## 557 HURRICANE IRMA
## 558 HURRICANE MARIA
## 559 HURRICANE HENRI
## 560 SEVERE WINTER STORM AND SNOWSTORM
## 561 HURRICANE SANDY
## 562 HURRICANE SANDY
## 563 COVID-19 PANDEMIC
## 564 HURRICANE IRMA
## 565 SEVERE STORMS AND FLOODING
## 566 HURRICANE FLORENCE
## 567 SEVERE STORMS AND FLOODING
## 568 HURRICANE DORIAN
## 569 HURRICANE MATTHEW
## 570 HURRICANE MATTHEW
## 571 COVID-19 PANDEMIC
## 572 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 573 COVID-19 PANDEMIC
## 574 SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 575 SEVERE STORMS, TORNADO, AND FLOODING
## 576 SEVERE WINTER STORM AND SNOWSTORM
## 577 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 578 SEVERE STORMS, TORNADOES, AND FLOODING
## 579 SEVERE WINTER STORM
## 580 COVID-19
## 581 SEVERE WINTER STORM, SNOWSTORM, AND FLOODING
## 582 WILDFIRES
## 583 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 584 COVID-19 PANDEMIC
## 585 SEVERE WINTER STORM AND FLOODING
## 586 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND
## 587 SEVERE STORM AND FLOODING
## 588 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 589 SEVERE WINTER STORMS
## 590 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS AND FLOODING
## 591 SEVERE STORMS AND FLOODING
## 592 SEVERE STORMS AND FLOODING
## 593 SEVERE STORMS AND FLOODING
## 594 SEVERE STORMS AND FLOODING
## 595 HURRICANE LAURA
## 596 HURRICANE HANNA
## 597 SEVERE STORMS, TORNADOES, AND FLOODING
## 598 HURRICANE HARVEY
## 599 EXPLOSION
## 600 SEVERE STORMS AND FLOODING
## 601 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 602 SEVERE WINTER STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 603 COVID-19 PANDEMIC
## 604 EXPLOSION
## 605 COVID-19 PANDEMIC
## 606 HURRICANE MATTHEW
## 607 HURRICANE FLORENCE
## 608 HURRICANE SANDY
## 609 COVID-19 PANDEMIC
## 610 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 611 HURRICANE FLORENCE
## 612 SEVERE STORM AND FLOODING
## 613 COVID-19
## 614 COVID-19 PANDEMIC
## 615 SEVERE STORMS AND FLOODING
## 616 SEVERE WINTER STORM, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 617 SEVERE WINTER STORMS, FLOODING, LANDSLIDES, MUDSLIDES
## 618 COVID-19 PANDEMIC
## 619 SEVERE STORMS, STRAIGHT-LINE WINDS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 620 WILDFIRES AND MUDSLIDES
## 621 SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 622 WILDFIRES
## 623 WILDFIRES
## 624 FLOODING AND MUDSLIDES
## 625 WILDFIRES
## 626 FLOODING AND MUDSLIDES
## 627 SEVERE STORMS AND FLOODING
## 628 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, FLOODING, AND LANDSLIDES
## 629 SEVERE STORMS, FLOODING, AND MUDSLIDES
## 630 COVID-19 PANDEMIC
## 631 SEVERE STORMS, FLOODING, AND MUDSLIDES
## 632 SEVERE STORMS, TORNADOES, STRAIGHT-LINE WINDS, AND FLOODING
## 633 SEVERE STORMS, STRAIGHT-LINE WINDS, AND FLOODING
## 634 SEVERE STORMS AND FLOODING
## 635 HURRICANE SANDY
## 636 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 637 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 638 SEVERE STORMS, FLOODING, LANDSLIDES, AND MUDSLIDES
## 639 HURRICANE SANDY
## 640 SEVERE STORMS, FLOODING, MUDSLIDES, AND LANSLIDES
## 641 COVID-19 PANDEMIC
## 642 SEVERE STORMS AND STRAIGHT-LINE WINDS
## 643 SEVERE STORMS, TORNADOES, FLOODING, MUDSLIDES, AND LANDSLIDES
## 644 SEVERE WINTER STORM, FLOODING, LANDSLIDES, AND MUDSLIDES
## 645 COVID-19 PANDEMIC
## 646 SEVERE STORMS AND FLOODING
## incidentType IncidentYear IncidentMonth IncidentDuration ResponseDays
## 1 Flood 2013 May 25.000000 3099.04167
## 2 Biological 2020 January 660.000000 660.00000
## 3 Earthquake 2018 November 0.000000 1076.00000
## 4 Severe Storm(s) 2012 September 15.000000 3343.04167
## 5 Biological 2020 January 660.000000 660.00000
## 6 Severe Storm(s) 2018 March 1.000000 1332.04167
## 7 Hurricane 2020 September 2.000000 422.04167
## 8 Severe Storm(s) 2014 April 7.000000 2753.04167
## 9 Severe Storm(s) 2015 May 39.000000 2379.04167
## 10 Severe Storm(s) 2014 April 0.000000 2754.04167
## 11 Severe Storm(s) 2017 April 23.000000 1659.04167
## 12 Severe Storm(s) 2015 December 27.000000 2146.00000
## 13 Severe Ice Storm 2013 December 1.000000 2897.00000
## 14 Biological 2020 January 660.000000 660.00000
## 15 Flood 2019 May 24.000000 904.04167
## 16 Severe Storm(s) 2016 March 5.000000 2073.00000
## 17 Biological 2020 January 660.000000 660.00000
## 18 Biological 2020 January 660.000000 660.00000
## 19 Biological 2020 January 660.000000 660.00000
## 20 Biological 2020 January 660.000000 660.00000
## 21 Fire 2017 December 25.000000 1437.00000
## 22 Other 2017 February 16.000000 1737.00000
## 23 Severe Storm(s) 2019 February 2.000000 1001.00000
## 24 Biological 2020 January 660.000000 660.00000
## 25 Fire 2020 September 74.041667 432.04167
## 26 Earthquake 2019 July 8.000000 860.04167
## 27 Fire 2018 July 58.000000 1206.04167
## 28 Fire 2017 December 58.000000 1437.00000
## 29 Fire 2020 August 43.000000 453.04167
## 30 Fire 2018 November 17.000000 1098.00000
## 31 Fire 2021 August 88.041667 88.04167
## 32 Fire 2018 July 58.000000 1206.04167
## 33 Severe Storm(s) 2017 January 9.000000 1772.00000
## 34 Fire 2018 November 17.000000 1098.00000
## 35 Fire 2015 September 51.000000 2254.04167
## 36 Fire 2017 October 23.000000 1494.04167
## 37 Fire 2021 July 119.041667 119.04167
## 38 Flood 2017 February 22.000000 1743.00000
## 39 Flood 2015 May 43.000000 2382.04167
## 40 Fire 2012 June 32.000000 2307.00000
## 41 Biological 2020 January 660.000000 660.00000
## 42 Flood 2013 September 19.000000 1213.04167
## 43 Flood 2013 September 19.000000 2982.04167
## 44 Hurricane 2020 August 0.000000 463.04167
## 45 Biological 2020 January 660.000000 660.00000
## 46 Hurricane 2012 October 12.041667 3301.04167
## 47 Hurricane 2020 August 0.000000 463.04167
## 48 Severe Storm(s) 2013 February 3.000000 3026.95833
## 49 Hurricane 2012 October 12.041667 1678.00000
## 50 Biological 2020 January 660.000000 660.00000
## 51 Other 2021 January 13.000000 303.00000
## 52 Biological 2020 January 660.000000 660.00000
## 53 Hurricane 2019 August 12.000000 762.00000
## 54 Other 2021 June 10.000000 139.04167
## 55 Hurricane 2016 October 16.000000 1029.00000
## 56 Hurricane 2018 October 12.000000 676.00000
## 57 Severe Storm(s) 2014 April 8.000000 2753.04167
## 58 Hurricane 2016 October 16.000000 1864.04167
## 59 Hurricane 2020 September 14.000000 422.04167
## 60 Severe Storm(s) 2012 June 33.000000 3427.04167
## 61 Hurricane 2017 September 44.000000 1528.04167
## 62 Hurricane 2017 September 30.000000 1528.04167
## 63 Biological 2020 January 660.000000 660.00000
## 64 Hurricane 2018 October 12.000000 1130.04167
## 65 Hurricane 2017 September 44.000000 105.04167
## 66 Hurricane 2019 August 9.000000 761.00000
## 67 Severe Storm(s) 2017 January 0.000000 1773.00000
## 68 Biological 2020 January 660.000000 660.00000
## 69 Hurricane 2016 October 11.000000 1863.04167
## 70 Tornado 2017 January 1.000000 1754.00000
## 71 Hurricane 2017 September 13.000000 1525.04167
## 72 Hurricane 2018 October 14.000000 1128.04167
## 73 Hurricane 2016 October 11.000000 561.00000
## 74 Hurricane 2018 August 7.000000 1176.04167
## 75 Biological 2020 January 660.000000 660.00000
## 76 Other 2018 May 106.000000 1287.04167
## 77 Flood 2018 April 3.000000 1307.04167
## 78 Hurricane 2020 July 4.000000 475.04167
## 79 Severe Storm(s) 2020 August 0.000000 457.04167
## 80 Severe Storm(s) 2013 May 26.000000 1859.00000
## 81 Flood 2013 April 13.000000 1834.00000
## 82 Flood 2019 March 95.000000 974.04167
## 83 Biological 2020 January 660.000000 660.00000
## 84 Flood 2017 March 21.958333 1710.00000
## 85 Severe Storm(s) 2015 November 0.000000 1777.95833
## 86 Biological 2020 January 660.000000 660.00000
## 87 Flood 2017 February 22.000000 1739.00000
## 88 Biological 2020 January 660.000000 660.00000
## 89 Tornado 2013 November 0.000000 1850.00000
## 90 Flood 2019 February 128.958333 990.00000
## 91 Flood 2013 April 19.000000 3130.04167
## 92 Severe Storm(s) 2012 February 3.000000 2714.95833
## 93 Flood 2018 February 18.000000 1365.00000
## 94 Biological 2020 January 660.000000 660.00000
## 95 Biological 2020 January 660.000000 660.00000
## 96 Severe Storm(s) 2019 May 64.000000 916.04167
## 97 Severe Storm(s) 2012 April 1.000000 3497.04167
## 98 Flood 2015 March 5.958333 2444.00000
## 99 Biological 2020 January 660.000000 660.00000
## 100 Severe Storm(s) 2012 February 3.000000 3542.00000
## 101 Flood 2018 February 5.000000 1370.00000
## 102 Severe Storm(s) 2019 February 32.000000 1008.00000
## 103 Hurricane 2020 October 4.000000 400.04167
## 104 Hurricane 2020 October 3.000000 380.04167
## 105 Other 2019 July 5.000000 854.04167
## 106 Flood 2016 August 20.000000 1917.04167
## 107 Hurricane 2020 August 5.000000 242.00000
## 108 Hurricane 2012 August 15.000000 3363.04167
## 109 Hurricane 2021 August 8.000000 76.04167
## 110 Hurricane 2017 October 3.000000 1497.04167
## 111 Hurricane 2020 August 5.000000 445.04167
## 112 Hurricane 2012 August 15.000000 1025.00000
## 113 Hurricane 2020 October 3.000000 380.04167
## 114 Flood 2016 March 30.958333 2073.00000
## 115 Hurricane 2020 September 3.000000 423.04167
## 116 Hurricane 2017 August 14.000000 263.00000
## 117 Biological 2020 January 660.000000 660.00000
## 118 Hurricane 2020 October 4.000000 400.04167
## 119 Other 2013 April 7.000000 1430.00000
## 120 Severe Storm(s) 2018 March 1.000000 1349.00000
## 121 Hurricane 2012 October 12.041667 698.00000
## 122 Severe Storm(s) 2015 January 2.000000 2480.00000
## 123 Biological 2020 January 660.000000 660.00000
## 124 Other 2018 March 1.000000 1338.04167
## 125 Biological 2020 January 660.000000 660.00000
## 126 Severe Storm(s) 2017 October 3.000000 1473.04167
## 127 Other 2020 May 6.000000 543.04167
## 128 Biological 2020 January 660.000000 660.00000
## 129 Severe Storm(s) 2021 June 1.000000 138.04167
## 130 Flood 2013 April 28.000000 2723.00000
## 131 Flood 2014 August 2.000000 2648.04167
## 132 Severe Storm(s) 2017 June 5.000000 1602.04167
## 133 Biological 2020 January 660.000000 464.95833
## 134 Flood 2019 March 47.000000 974.04167
## 135 Severe Storm(s) 2013 June 6.000000 2580.00000
## 136 Flood 2016 September 3.000000 1876.04167
## 137 Severe Storm(s) 2013 April 2.000000 2443.04167
## 138 Flood 2018 October 2.000000 1128.04167
## 139 Severe Storm(s) 2012 June 7.000000 3436.04167
## 140 Biological 2020 January 660.000000 660.00000
## 141 Flood 2018 June 27.000000 1244.04167
## 142 Flood 2014 June 30.000000 2709.04167
## 143 Flood 2017 April 13.000000 1657.04167
## 144 Biological 2020 January 660.000000 660.00000
## 145 Flood 2015 December 18.000000 1987.95833
## 146 Severe Storm(s) 2015 May 73.000000 2250.00000
## 147 Flood 2015 December 17.000000 2149.00000
## 148 Flood 2019 March 36.000000 975.04167
## 149 Severe Storm(s) 2019 April 67.000000 926.04167
## 150 Severe Storm(s) 2014 April 5.000000 2753.04167
## 151 Hurricane 2012 August 16.000000 2832.00000
## 152 Severe Storm(s) 2020 April 0.000000 577.04167
## 153 Hurricane 2021 August 4.000000 74.04167
## 154 Tornado 2017 January 1.000000 1755.00000
## 155 Flood 2016 March 19.958333 2072.00000
## 156 Biological 2020 January 660.000000 660.00000
## 157 Severe Storm(s) 2015 December 5.000000 2149.00000
## 158 Flood 2013 May 15.000000 2689.00000
## 159 Biological 2020 January 660.000000 660.00000
## 160 Severe Storm(s) 2020 November 3.000000 363.00000
## 161 Hurricane 2016 October 20.000000 561.00000
## 162 Hurricane 2019 September 8.000000 319.00000
## 163 Hurricane 2019 September 8.000000 801.04167
## 164 Hurricane 2018 September 22.000000 1160.04167
## 165 Biological 2020 January 660.000000 660.00000
## 166 Hurricane 2016 October 20.000000 1863.04167
## 167 Flood 2013 July 10.000000 3052.04167
## 168 Hurricane 2018 October 2.000000 1127.04167
## 169 Flood 2014 June 6.000000 2050.04167
## 170 Flood 2017 March 37.000000 1693.04167
## 171 Flood 2013 April 15.000000 1037.04167
## 172 Biological 2020 January 660.000000 660.00000
## 173 Flood 2019 March 19.000000 973.04167
## 174 Severe Storm(s) 2014 June 7.000000 2699.00000
## 175 Biological 2020 January 660.000000 660.00000
## 176 Severe Storm(s) 2015 May 42.000000 2380.04167
## 177 Flood 2019 March 126.958333 977.00000
## 178 Other 2018 March 6.000000 1349.00000
## 179 Severe Storm(s) 2013 February 2.000000 2727.95833
## 180 Severe Storm(s) 2017 October 3.000000 1473.04167
## 181 Biological 2020 January 660.000000 660.00000
## 182 Hurricane 2012 October 13.041667 3302.04167
## 183 Biological 2020 January 660.000000 660.00000
## 184 Hurricane 2021 September 2.000000 70.04167
## 185 Flood 2017 October 2.000000 1498.04167
## 186 Severe Storm(s) 2013 September 3.000000 2980.04167
## 187 Biological 2020 January 660.000000 660.00000
## 188 Biological 2020 January 660.000000 660.00000
## 189 Severe Storm(s) 2017 February 17.000000 1739.00000
## 190 Severe Storm(s) 2017 January 9.000000 1770.00000
## 191 Hurricane 2021 August 3.000000 81.04167
## 192 Biological 2020 January 660.000000 660.00000
## 193 Hurricane 2012 October 12.041667 3301.04167
## 194 Flood 2013 June 14.000000 3059.04167
## 195 Hurricane 2021 September 2.000000 70.04167
## 196 Severe Storm(s) 2014 May 9.000000 2738.04167
## 197 Biological 2020 January 660.000000 660.00000
## 198 Severe Storm(s) 2012 June 3.000000 889.04167
## 199 Severe Storm(s) 2012 June 3.000000 2035.04167
## 200 Tornado 2019 May 2.000000 898.04167
## 201 Flood 2018 February 11.000000 1365.00000
## 202 Hurricane 2012 October 1.000000 2893.00000
## 203 Tornado 2013 May 15.000000 3098.04167
## 204 Biological 2020 January 660.000000 660.00000
## 205 Severe Storm(s) 2015 May 48.000000 2381.04167
## 206 Biological 2020 January 660.000000 660.00000
## 207 Tornado 2017 May 4.000000 1639.04167
## 208 Biological 2020 January 660.000000 660.00000
## 209 Severe Storm(s) 2017 April 4.000000 1657.04167
## 210 Biological 2020 January 660.000000 660.00000
## 211 Fire 2012 August 11.000000 2512.00000
## 212 Severe Storm(s) 2019 May 33.000000 918.04167
## 213 Severe Storm(s) 2019 February 3.000000 991.00000
## 214 Flood 2019 April 15.000000 949.04167
## 215 Severe Storm(s) 2016 December 3.000000 1792.00000
## 216 Severe Storm(s) 2017 January 3.000000 1768.00000
## 217 Fire 2020 September 57.041667 429.04167
## 218 Flood 2012 January 4.000000 2765.95833
## 219 Biological 2020 January 660.000000 660.00000
## 220 Severe Storm(s) 2015 December 17.000000 2166.00000
## 221 Severe Storm(s) 2013 June 15.000000 2045.04167
## 222 Hurricane 2012 October 13.041667 993.00000
## 223 Hurricane 2021 August 5.000000 71.04167
## 224 Biological 2020 January 660.000000 660.00000
## 225 Severe Storm(s) 2018 August 5.000000 1188.04167
## 226 Severe Ice Storm 2014 February 16.000000 477.95833
## 227 Earthquake 2019 December 38.000000 683.00000
## 228 Flood 2020 September 0.000000 423.04167
## 229 Earthquake 2019 December 187.958333 683.00000
## 230 Hurricane 2020 July 4.000000 471.04167
## 231 Biological 2020 January 660.000000 660.00000
## 232 Severe Storm(s) 2019 August 11.000000 807.04167
## 233 Hurricane 2017 September 59.041667 1515.04167
## 234 Hurricane 2017 September 2.000000 1527.04167
## 235 Hurricane 2017 September 59.041667 355.00000
## 236 Hurricane 2021 August 4.000000 82.04167
## 237 Severe Storm(s) 2013 February 1.000000 3197.00000
## 238 Hurricane 2012 October 13.041667 158.00000
## 239 Hurricane 2012 October 5.000000 3302.04167
## 240 Biological 2020 January 660.000000 660.00000
## 241 Hurricane 2017 September 7.000000 315.00000
## 242 Severe Storm(s) 2015 October 22.000000 809.04167
## 243 Hurricane 2018 September 30.000000 1159.04167
## 244 Flood 2015 October 22.000000 2232.04167
## 245 Hurricane 2019 August 6.000000 622.00000
## 246 Hurricane 2016 October 26.000000 442.04167
## 247 Hurricane 2016 October 26.000000 1863.04167
## 248 Biological 2020 January 660.000000 660.00000
## 249 Severe Storm(s) 2015 May 21.000000 2378.04167
## 250 Biological 2020 January 660.000000 660.00000
## 251 Severe Storm(s) 2013 October 13.000000 2960.04167
## 252 Severe Storm(s) 2013 May 7.000000 1586.00000
## 253 Severe Storm(s) 2013 April 2.000000 1632.00000
## 254 Severe Storm(s) 2015 June 7.000000 1668.04167
## 255 Flood 2014 June 7.000000 1930.00000
## 256 Severe Storm(s) 2016 December 2.000000 1782.00000
## 257 Biological 2020 January 660.000000 660.00000
## 258 Flood 2019 March 44.000000 973.04167
## 259 Fire 2016 November 11.000000 1808.00000
## 260 Tornado 2020 March 0.000000 617.00000
## 261 Biological 2020 January 660.000000 660.00000
## 262 Severe Ice Storm 2015 February 7.000000 2460.00000
## 263 Severe Storm(s) 2012 February 2.000000 1225.95833
## 264 Flood 2021 August 0.000000 81.04167
## 265 Severe Storm(s) 2014 June 5.000000 2498.00000
## 266 Severe Ice Storm 2021 February 10.000000 272.00000
## 267 Severe Storm(s) 2015 May 49.000000 2382.04167
## 268 Severe Storm(s) 2013 October 1.000000 2933.04167
## 269 Flood 2016 April 13.000000 2033.04167
## 270 Flood 2016 May 33.000000 1998.04167
## 271 Flood 2018 September 53.000000 1157.04167
## 272 Hurricane 2020 August 4.000000 444.04167
## 273 Hurricane 2020 July 6.000000 473.04167
## 274 Flood 2016 March 21.958333 2074.00000
## 275 Hurricane 2017 August 23.000000 1540.04167
## 276 Other 2013 April 3.000000 3129.04167
## 277 Flood 2018 June 24.000000 1240.04167
## 278 Severe Storm(s) 2015 October 9.000000 2211.04167
## 279 Severe Storm(s) 2015 December 26.000000 2146.00000
## 280 Biological 2020 January 660.000000 660.00000
## 281 Other 2013 April 3.000000 924.00000
## 282 Biological 2020 January 660.000000 660.00000
## 283 Hurricane 2016 October 8.000000 1860.04167
## 284 Hurricane 2018 September 13.000000 1159.04167
## 285 Hurricane 2012 October 13.041667 2860.00000
## 286 Biological 2020 January 660.000000 660.00000
## 287 Severe Storm(s) 2012 June 2.000000 2727.04167
## 288 Hurricane 2018 September 8.000000 697.00000
## 289 Severe Storm(s) 2017 October 1.000000 1473.04167
## 290 Biological 2020 January 660.000000 660.00000
## 291 Biological 2020 January 660.000000 660.00000
## 292 Flood 2019 April 0.000000 940.04167
## 293 Severe Storm(s) 2020 December 18.000000 316.00000
## 294 Flood 2017 January 23.000000 1745.00000
## 295 Biological 2020 January 660.000000 660.00000
## 296 Severe Storm(s) 2015 November 9.000000 2190.00000
## 297 Fire 2015 August 32.000000 2285.04167
## 298 Severe Storm(s) 2012 January 9.000000 3588.00000
## 299 Fire 2014 July 27.000000 653.00000
## 300 Fire 2015 August 28.000000 966.00000
## 301 Other 2014 March 37.000000 1370.04167
## 302 Fire 2014 July 27.000000 2681.04167
## 303 Other 2014 March 37.000000 2790.04167
## 304 Severe Storm(s) 2012 June 1.000000 2458.00000
## 305 Flood 2018 August 28.000000 1181.04167
## 306 Flood 2016 September 1.000000 1876.04167
## 307 Biological 2020 January 660.000000 660.00000
## 308 Severe Storm(s) 2013 June 8.000000 2885.00000
## 309 Flood 2019 July 2.000000 846.04167
## 310 Flood 2018 June 4.000000 1244.04167
## 311 Severe Storm(s) 2016 July 1.000000 1948.04167
## 312 Hurricane 2012 October 10.041667 899.00000
## 313 Flood 2016 June 7.000000 1967.04167
## 314 Flood 2015 April 2.000000 2403.04167
## 315 Other 2018 February 6.000000 1365.00000
## 316 Hurricane 2012 October 10.041667 2320.04167
## 317 Severe Storm(s) 2012 March 16.000000 2513.04167
## 318 Biological 2020 January 660.000000 660.00000
## 319 Severe Storm(s) 2012 June 9.000000 2727.04167
## 320 Severe Storm(s) 2012 February 5.000000 2267.95833
## 321 Severe Storm(s) 2015 March 10.958333 2444.00000
## 322 Biological 2020 January 660.000000 660.00000
## 323 Flood 2015 May 13.000000 2249.00000
## 324 Flood 2013 May 25.000000 3099.04167
## 325 Biological 2020 January 660.000000 660.00000
## 326 Earthquake 2018 November 0.000000 1076.00000
## 327 Severe Storm(s) 2012 September 15.000000 3343.04167
## 328 Biological 2020 January 660.000000 660.00000
## 329 Severe Storm(s) 2018 March 1.000000 1332.04167
## 330 Hurricane 2020 September 2.000000 422.04167
## 331 Severe Storm(s) 2014 April 7.000000 2753.04167
## 332 Severe Storm(s) 2015 May 39.000000 2379.04167
## 333 Severe Storm(s) 2014 April 0.000000 2754.04167
## 334 Severe Storm(s) 2017 April 23.000000 1659.04167
## 335 Severe Storm(s) 2015 December 27.000000 2146.00000
## 336 Severe Ice Storm 2013 December 1.000000 2897.00000
## 337 Biological 2020 January 660.000000 660.00000
## 338 Flood 2019 May 24.000000 904.04167
## 339 Severe Storm(s) 2016 March 5.000000 2073.00000
## 340 Biological 2020 January 660.000000 660.00000
## 341 Biological 2020 January 660.000000 660.00000
## 342 Biological 2020 January 660.000000 660.00000
## 343 Biological 2020 January 660.000000 660.00000
## 344 Fire 2017 December 25.000000 1437.00000
## 345 Other 2017 February 16.000000 1737.00000
## 346 Severe Storm(s) 2019 February 2.000000 1001.00000
## 347 Biological 2020 January 660.000000 660.00000
## 348 Fire 2020 September 74.041667 432.04167
## 349 Earthquake 2019 July 8.000000 860.04167
## 350 Fire 2018 July 58.000000 1206.04167
## 351 Fire 2017 December 58.000000 1437.00000
## 352 Fire 2020 August 43.000000 453.04167
## 353 Fire 2018 November 17.000000 1098.00000
## 354 Fire 2021 August 88.041667 88.04167
## 355 Fire 2018 July 58.000000 1206.04167
## 356 Severe Storm(s) 2017 January 9.000000 1772.00000
## 357 Fire 2018 November 17.000000 1098.00000
## 358 Fire 2015 September 51.000000 2254.04167
## 359 Fire 2017 October 23.000000 1494.04167
## 360 Fire 2021 July 119.041667 119.04167
## 361 Flood 2017 February 22.000000 1743.00000
## 362 Flood 2015 May 43.000000 2382.04167
## 363 Fire 2012 June 32.000000 2307.00000
## 364 Biological 2020 January 660.000000 660.00000
## 365 Flood 2013 September 19.000000 1213.04167
## 366 Flood 2013 September 19.000000 2982.04167
## 367 Hurricane 2020 August 0.000000 463.04167
## 368 Biological 2020 January 660.000000 660.00000
## 369 Hurricane 2012 October 12.041667 3301.04167
## 370 Hurricane 2020 August 0.000000 463.04167
## 371 Severe Storm(s) 2013 February 3.000000 3026.95833
## 372 Hurricane 2012 October 12.041667 1678.00000
## 373 Biological 2020 January 660.000000 660.00000
## 374 Other 2021 January 13.000000 303.00000
## 375 Biological 2020 January 660.000000 660.00000
## 376 Hurricane 2019 August 12.000000 762.00000
## 377 Other 2021 June 10.000000 139.04167
## 378 Hurricane 2016 October 16.000000 1029.00000
## 379 Hurricane 2018 October 12.000000 676.00000
## 380 Severe Storm(s) 2014 April 8.000000 2753.04167
## 381 Hurricane 2016 October 16.000000 1864.04167
## 382 Hurricane 2020 September 14.000000 422.04167
## 383 Severe Storm(s) 2012 June 33.000000 3427.04167
## 384 Hurricane 2017 September 44.000000 1528.04167
## 385 Hurricane 2017 September 30.000000 1528.04167
## 386 Biological 2020 January 660.000000 660.00000
## 387 Hurricane 2018 October 12.000000 1130.04167
## 388 Hurricane 2017 September 44.000000 105.04167
## 389 Hurricane 2019 August 9.000000 761.00000
## 390 Severe Storm(s) 2017 January 0.000000 1773.00000
## 391 Biological 2020 January 660.000000 660.00000
## 392 Hurricane 2016 October 11.000000 1863.04167
## 393 Tornado 2017 January 1.000000 1754.00000
## 394 Hurricane 2017 September 13.000000 1525.04167
## 395 Hurricane 2018 October 14.000000 1128.04167
## 396 Hurricane 2016 October 11.000000 561.00000
## 397 Hurricane 2018 August 7.000000 1176.04167
## 398 Biological 2020 January 660.000000 660.00000
## 399 Other 2018 May 106.000000 1287.04167
## 400 Flood 2018 April 3.000000 1307.04167
## 401 Hurricane 2020 July 4.000000 475.04167
## 402 Severe Storm(s) 2020 August 0.000000 457.04167
## 403 Severe Storm(s) 2013 May 26.000000 1859.00000
## 404 Flood 2013 April 13.000000 1834.00000
## 405 Flood 2019 March 95.000000 974.04167
## 406 Biological 2020 January 660.000000 660.00000
## 407 Flood 2017 March 21.958333 1710.00000
## 408 Severe Storm(s) 2015 November 0.000000 1777.95833
## 409 Biological 2020 January 660.000000 660.00000
## 410 Flood 2017 February 22.000000 1739.00000
## 411 Biological 2020 January 660.000000 660.00000
## 412 Tornado 2013 November 0.000000 1850.00000
## 413 Flood 2019 February 128.958333 990.00000
## 414 Flood 2013 April 19.000000 3130.04167
## 415 Severe Storm(s) 2012 February 3.000000 2714.95833
## 416 Flood 2018 February 18.000000 1365.00000
## 417 Biological 2020 January 660.000000 660.00000
## 418 Biological 2020 January 660.000000 660.00000
## 419 Severe Storm(s) 2019 May 64.000000 916.04167
## 420 Severe Storm(s) 2012 April 1.000000 3497.04167
## 421 Flood 2015 March 5.958333 2444.00000
## 422 Biological 2020 January 660.000000 660.00000
## 423 Severe Storm(s) 2012 February 3.000000 3542.00000
## 424 Flood 2018 February 5.000000 1370.00000
## 425 Severe Storm(s) 2019 February 32.000000 1008.00000
## 426 Hurricane 2020 October 4.000000 400.04167
## 427 Hurricane 2020 October 3.000000 380.04167
## 428 Other 2019 July 5.000000 854.04167
## 429 Flood 2016 August 20.000000 1917.04167
## 430 Hurricane 2020 August 5.000000 242.00000
## 431 Hurricane 2012 August 15.000000 3363.04167
## 432 Hurricane 2021 August 8.000000 76.04167
## 433 Hurricane 2017 October 3.000000 1497.04167
## 434 Hurricane 2020 August 5.000000 445.04167
## 435 Hurricane 2012 August 15.000000 1025.00000
## 436 Hurricane 2020 October 3.000000 380.04167
## 437 Flood 2016 March 30.958333 2073.00000
## 438 Hurricane 2020 September 3.000000 423.04167
## 439 Hurricane 2017 August 14.000000 263.00000
## 440 Biological 2020 January 660.000000 660.00000
## 441 Hurricane 2020 October 4.000000 400.04167
## 442 Other 2013 April 7.000000 1430.00000
## 443 Severe Storm(s) 2018 March 1.000000 1349.00000
## 444 Hurricane 2012 October 12.041667 698.00000
## 445 Severe Storm(s) 2015 January 2.000000 2480.00000
## 446 Biological 2020 January 660.000000 660.00000
## 447 Other 2018 March 1.000000 1338.04167
## 448 Biological 2020 January 660.000000 660.00000
## 449 Severe Storm(s) 2017 October 3.000000 1473.04167
## 450 Other 2020 May 6.000000 543.04167
## 451 Biological 2020 January 660.000000 660.00000
## 452 Severe Storm(s) 2021 June 1.000000 138.04167
## 453 Flood 2013 April 28.000000 2723.00000
## 454 Flood 2014 August 2.000000 2648.04167
## 455 Severe Storm(s) 2017 June 5.000000 1602.04167
## 456 Biological 2020 January 660.000000 464.95833
## 457 Flood 2019 March 47.000000 974.04167
## 458 Severe Storm(s) 2013 June 6.000000 2580.00000
## 459 Flood 2016 September 3.000000 1876.04167
## 460 Severe Storm(s) 2013 April 2.000000 2443.04167
## 461 Flood 2018 October 2.000000 1128.04167
## 462 Severe Storm(s) 2012 June 7.000000 3436.04167
## 463 Biological 2020 January 660.000000 660.00000
## 464 Flood 2018 June 27.000000 1244.04167
## 465 Flood 2014 June 30.000000 2709.04167
## 466 Flood 2017 April 13.000000 1657.04167
## 467 Biological 2020 January 660.000000 660.00000
## 468 Flood 2015 December 18.000000 1987.95833
## 469 Severe Storm(s) 2015 May 73.000000 2250.00000
## 470 Flood 2015 December 17.000000 2149.00000
## 471 Flood 2019 March 36.000000 975.04167
## 472 Severe Storm(s) 2019 April 67.000000 926.04167
## 473 Severe Storm(s) 2014 April 5.000000 2753.04167
## 474 Hurricane 2012 August 16.000000 2832.00000
## 475 Severe Storm(s) 2020 April 0.000000 577.04167
## 476 Hurricane 2021 August 4.000000 74.04167
## 477 Tornado 2017 January 1.000000 1755.00000
## 478 Flood 2016 March 19.958333 2072.00000
## 479 Biological 2020 January 660.000000 660.00000
## 480 Severe Storm(s) 2015 December 5.000000 2149.00000
## 481 Flood 2013 May 15.000000 2689.00000
## 482 Biological 2020 January 660.000000 660.00000
## 483 Severe Storm(s) 2020 November 3.000000 363.00000
## 484 Hurricane 2016 October 20.000000 561.00000
## 485 Hurricane 2019 September 8.000000 319.00000
## 486 Hurricane 2019 September 8.000000 801.04167
## 487 Hurricane 2018 September 22.000000 1160.04167
## 488 Biological 2020 January 660.000000 660.00000
## 489 Hurricane 2016 October 20.000000 1863.04167
## 490 Flood 2013 July 10.000000 3052.04167
## 491 Hurricane 2018 October 2.000000 1127.04167
## 492 Flood 2014 June 6.000000 2050.04167
## 493 Flood 2017 March 37.000000 1693.04167
## 494 Flood 2013 April 15.000000 1037.04167
## 495 Biological 2020 January 660.000000 660.00000
## 496 Flood 2019 March 19.000000 973.04167
## 497 Severe Storm(s) 2014 June 7.000000 2699.00000
## 498 Biological 2020 January 660.000000 660.00000
## 499 Severe Storm(s) 2015 May 42.000000 2380.04167
## 500 Flood 2019 March 126.958333 977.00000
## 501 Other 2018 March 6.000000 1349.00000
## 502 Severe Storm(s) 2013 February 2.000000 2727.95833
## 503 Severe Storm(s) 2017 October 3.000000 1473.04167
## 504 Biological 2020 January 660.000000 660.00000
## 505 Hurricane 2012 October 13.041667 3302.04167
## 506 Biological 2020 January 660.000000 660.00000
## 507 Hurricane 2021 September 2.000000 70.04167
## 508 Flood 2017 October 2.000000 1498.04167
## 509 Severe Storm(s) 2013 September 3.000000 2980.04167
## 510 Biological 2020 January 660.000000 660.00000
## 511 Biological 2020 January 660.000000 660.00000
## 512 Severe Storm(s) 2017 February 17.000000 1739.00000
## 513 Severe Storm(s) 2017 January 9.000000 1770.00000
## 514 Hurricane 2021 August 3.000000 81.04167
## 515 Biological 2020 January 660.000000 660.00000
## 516 Hurricane 2012 October 12.041667 3301.04167
## 517 Flood 2013 June 14.000000 3059.04167
## 518 Hurricane 2021 September 2.000000 70.04167
## 519 Severe Storm(s) 2014 May 9.000000 2738.04167
## 520 Biological 2020 January 660.000000 660.00000
## 521 Severe Storm(s) 2012 June 3.000000 889.04167
## 522 Severe Storm(s) 2012 June 3.000000 2035.04167
## 523 Tornado 2019 May 2.000000 898.04167
## 524 Flood 2018 February 11.000000 1365.00000
## 525 Hurricane 2012 October 1.000000 2893.00000
## 526 Tornado 2013 May 15.000000 3098.04167
## 527 Biological 2020 January 660.000000 660.00000
## 528 Severe Storm(s) 2015 May 48.000000 2381.04167
## 529 Biological 2020 January 660.000000 660.00000
## 530 Tornado 2017 May 4.000000 1639.04167
## 531 Biological 2020 January 660.000000 660.00000
## 532 Severe Storm(s) 2017 April 4.000000 1657.04167
## 533 Biological 2020 January 660.000000 660.00000
## 534 Fire 2012 August 11.000000 2512.00000
## 535 Severe Storm(s) 2019 May 33.000000 918.04167
## 536 Severe Storm(s) 2019 February 3.000000 991.00000
## 537 Flood 2019 April 15.000000 949.04167
## 538 Severe Storm(s) 2016 December 3.000000 1792.00000
## 539 Severe Storm(s) 2017 January 3.000000 1768.00000
## 540 Fire 2020 September 57.041667 429.04167
## 541 Flood 2012 January 4.000000 2765.95833
## 542 Biological 2020 January 660.000000 660.00000
## 543 Severe Storm(s) 2015 December 17.000000 2166.00000
## 544 Severe Storm(s) 2013 June 15.000000 2045.04167
## 545 Hurricane 2012 October 13.041667 993.00000
## 546 Hurricane 2021 August 5.000000 71.04167
## 547 Biological 2020 January 660.000000 660.00000
## 548 Severe Storm(s) 2018 August 5.000000 1188.04167
## 549 Severe Ice Storm 2014 February 16.000000 477.95833
## 550 Earthquake 2019 December 38.000000 683.00000
## 551 Flood 2020 September 0.000000 423.04167
## 552 Earthquake 2019 December 187.958333 683.00000
## 553 Hurricane 2020 July 4.000000 471.04167
## 554 Biological 2020 January 660.000000 660.00000
## 555 Severe Storm(s) 2019 August 11.000000 807.04167
## 556 Hurricane 2017 September 59.041667 1515.04167
## 557 Hurricane 2017 September 2.000000 1527.04167
## 558 Hurricane 2017 September 59.041667 355.00000
## 559 Hurricane 2021 August 4.000000 82.04167
## 560 Severe Storm(s) 2013 February 1.000000 3197.00000
## 561 Hurricane 2012 October 13.041667 158.00000
## 562 Hurricane 2012 October 5.000000 3302.04167
## 563 Biological 2020 January 660.000000 660.00000
## 564 Hurricane 2017 September 7.000000 315.00000
## 565 Severe Storm(s) 2015 October 22.000000 809.04167
## 566 Hurricane 2018 September 30.000000 1159.04167
## 567 Flood 2015 October 22.000000 2232.04167
## 568 Hurricane 2019 August 6.000000 622.00000
## 569 Hurricane 2016 October 26.000000 442.04167
## 570 Hurricane 2016 October 26.000000 1863.04167
## 571 Biological 2020 January 660.000000 660.00000
## 572 Severe Storm(s) 2015 May 21.000000 2378.04167
## 573 Biological 2020 January 660.000000 660.00000
## 574 Severe Storm(s) 2013 October 13.000000 2960.04167
## 575 Severe Storm(s) 2013 May 7.000000 1586.00000
## 576 Severe Storm(s) 2013 April 2.000000 1632.00000
## 577 Severe Storm(s) 2015 June 7.000000 1668.04167
## 578 Flood 2014 June 7.000000 1930.00000
## 579 Severe Storm(s) 2016 December 2.000000 1782.00000
## 580 Biological 2020 January 660.000000 660.00000
## 581 Flood 2019 March 44.000000 973.04167
## 582 Fire 2016 November 11.000000 1808.00000
## 583 Tornado 2020 March 0.000000 617.00000
## 584 Biological 2020 January 660.000000 660.00000
## 585 Severe Ice Storm 2015 February 7.000000 2460.00000
## 586 Severe Storm(s) 2012 February 2.000000 1225.95833
## 587 Flood 2021 August 0.000000 81.04167
## 588 Severe Storm(s) 2014 June 5.000000 2498.00000
## 589 Severe Ice Storm 2021 February 10.000000 272.00000
## 590 Severe Storm(s) 2015 May 49.000000 2382.04167
## 591 Severe Storm(s) 2013 October 1.000000 2933.04167
## 592 Flood 2016 April 13.000000 2033.04167
## 593 Flood 2016 May 33.000000 1998.04167
## 594 Flood 2018 September 53.000000 1157.04167
## 595 Hurricane 2020 August 4.000000 444.04167
## 596 Hurricane 2020 July 6.000000 473.04167
## 597 Flood 2016 March 21.958333 2074.00000
## 598 Hurricane 2017 August 23.000000 1540.04167
## 599 Other 2013 April 3.000000 3129.04167
## 600 Flood 2018 June 24.000000 1240.04167
## 601 Severe Storm(s) 2015 October 9.000000 2211.04167
## 602 Severe Storm(s) 2015 December 26.000000 2146.00000
## 603 Biological 2020 January 660.000000 660.00000
## 604 Other 2013 April 3.000000 924.00000
## 605 Biological 2020 January 660.000000 660.00000
## 606 Hurricane 2016 October 8.000000 1860.04167
## 607 Hurricane 2018 September 13.000000 1159.04167
## 608 Hurricane 2012 October 13.041667 2860.00000
## 609 Biological 2020 January 660.000000 660.00000
## 610 Severe Storm(s) 2012 June 2.000000 2727.04167
## 611 Hurricane 2018 September 8.000000 697.00000
## 612 Severe Storm(s) 2017 October 1.000000 1473.04167
## 613 Biological 2020 January 660.000000 660.00000
## 614 Biological 2020 January 660.000000 660.00000
## 615 Flood 2019 April 0.000000 940.04167
## 616 Severe Storm(s) 2020 December 18.000000 316.00000
## 617 Flood 2017 January 23.000000 1745.00000
## 618 Biological 2020 January 660.000000 660.00000
## 619 Severe Storm(s) 2015 November 9.000000 2190.00000
## 620 Fire 2015 August 32.000000 2285.04167
## 621 Severe Storm(s) 2012 January 9.000000 3588.00000
## 622 Fire 2014 July 27.000000 653.00000
## 623 Fire 2015 August 28.000000 966.00000
## 624 Other 2014 March 37.000000 1370.04167
## 625 Fire 2014 July 27.000000 2681.04167
## 626 Other 2014 March 37.000000 2790.04167
## 627 Severe Storm(s) 2012 June 1.000000 2458.00000
## 628 Flood 2018 August 28.000000 1181.04167
## 629 Flood 2016 September 1.000000 1876.04167
## 630 Biological 2020 January 660.000000 660.00000
## 631 Severe Storm(s) 2013 June 8.000000 2885.00000
## 632 Flood 2019 July 2.000000 846.04167
## 633 Flood 2018 June 4.000000 1244.04167
## 634 Severe Storm(s) 2016 July 1.000000 1948.04167
## 635 Hurricane 2012 October 10.041667 899.00000
## 636 Flood 2016 June 7.000000 1967.04167
## 637 Flood 2015 April 2.000000 2403.04167
## 638 Other 2018 February 6.000000 1365.00000
## 639 Hurricane 2012 October 10.041667 2320.04167
## 640 Severe Storm(s) 2012 March 16.000000 2513.04167
## 641 Biological 2020 January 660.000000 660.00000
## 642 Severe Storm(s) 2012 June 9.000000 2727.04167
## 643 Severe Storm(s) 2012 February 5.000000 2267.95833
## 644 Severe Storm(s) 2015 March 10.958333 2444.00000
## 645 Biological 2020 January 660.000000 660.00000
## 646 Flood 2015 May 13.000000 2249.00000
## IH IH_pct PA PA_pct HM HM_pct
## 1 Awarded 0.117647059 Awarded 0.147058824 Awarded 0.147058824
## 2 Awarded 2.470588235 Awarded 2.470588235 Awarded 1.000000000
## 3 Awarded 0.088235294 Awarded 0.088235294 Awarded 0.088235294
## 4 Not Awarded 0.000000000 Awarded 0.147058824 Awarded 0.147058824
## 5 Awarded 1.029850746 Awarded 1.029850746 Awarded 1.029850746
## 6 Awarded 0.044776119 Awarded 0.059701493 Awarded 0.059701493
## 7 Awarded 0.044776119 Awarded 0.223880597 Awarded 0.223880597
## 8 Awarded 0.134328358 Awarded 0.313432836 Awarded 0.313432836
## 9 Awarded 0.120000000 Awarded 0.373333333 Awarded 0.386666667
## 10 Awarded 0.053333333 Awarded 0.146666667 Awarded 0.160000000
## 11 Awarded 0.213333333 Awarded 0.400000000 Awarded 0.426666667
## 12 Awarded 0.146666667 Awarded 0.426666667 Awarded 0.506666667
## 13 Not Awarded 0.000000000 Awarded 0.186666667 Awarded 0.186666667
## 14 Awarded 1.013333333 Awarded 1.013333333 Awarded 1.013333333
## 15 Awarded 0.173333333 Awarded 0.226666667 Awarded 0.213333333
## 16 Not Awarded 0.000000000 Awarded 0.160000000 Awarded 0.160000000
## 17 Not Awarded 0.000000000 Awarded 2.400000000 Not Awarded 0.000000000
## 18 Awarded 2.400000000 Awarded 2.400000000 Awarded 2.400000000
## 19 Awarded 0.066666667 Awarded 0.066666667 Awarded 0.066666667
## 20 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 21 Not Awarded 0.000000000 Awarded 0.086206897 Not Awarded 0.000000000
## 22 Not Awarded 0.000000000 Awarded 0.051724138 Not Awarded 0.000000000
## 23 Not Awarded 0.000000000 Awarded 0.189655172 Awarded 0.189655172
## 24 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 25 Awarded 0.172413793 Awarded 0.206896552 Awarded 0.206896552
## 26 Not Awarded 0.000000000 Awarded 0.034482759 Not Awarded 0.000000000
## 27 Awarded 0.034482759 Awarded 0.034482759 Awarded 0.034482759
## 28 Awarded 0.068965517 Awarded 0.034482759 Awarded 0.068965517
## 29 Awarded 0.258620690 Awarded 0.344827586 Awarded 0.327586207
## 30 Awarded 0.051724138 Awarded 0.051724138 Awarded 0.051724138
## 31 Not Awarded 0.000000000 Awarded 0.017241379 Awarded 0.017241379
## 32 Not Awarded 0.000000000 Awarded 0.017241379 Not Awarded 0.000000000
## 33 Not Awarded 0.000000000 Awarded 0.586206897 Awarded 0.586206897
## 34 Not Awarded 0.000000000 Awarded 0.051724138 Not Awarded 0.000000000
## 35 Awarded 0.034482759 Awarded 0.034482759 Awarded 0.034482759
## 36 Awarded 0.137931034 Awarded 0.155172414 Awarded 0.155172414
## 37 Awarded 0.103448276 Awarded 0.103448276 Awarded 0.103448276
## 38 Not Awarded 0.000000000 Awarded 0.758620690 Awarded 0.758620690
## 39 Not Awarded 0.000000000 Awarded 0.234375000 Awarded 0.234375000
## 40 Awarded 0.031250000 Awarded 0.046875000 Awarded 0.046875000
## 41 Awarded 1.031250000 Awarded 1.031250000 Awarded 1.031250000
## 42 Not Awarded 0.000000000 Awarded 0.234375000 Not Awarded 0.000000000
## 43 Awarded 0.171875000 Awarded 0.281250000 Awarded 0.281250000
## 44 Not Awarded 0.000000000 Awarded 1.250000000 Not Awarded 0.000000000
## 45 Awarded 1.500000000 Awarded 1.500000000 Awarded 1.500000000
## 46 Awarded 0.625000000 Awarded 1.000000000 Awarded 1.000000000
## 47 Not Awarded 0.000000000 Awarded 1.250000000 Awarded 1.250000000
## 48 Not Awarded 0.000000000 Awarded 1.125000000 Awarded 1.125000000
## 49 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 50 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 51 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 52 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 53 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 54 Awarded 0.014925373 Awarded 0.014925373 Not Awarded 0.000000000
## 55 Not Awarded 0.000000000 Awarded 0.417910448 Not Awarded 0.000000000
## 56 Not Awarded 0.000000000 Awarded 0.522388060 Not Awarded 0.000000000
## 57 Awarded 0.074626866 Awarded 0.134328358 Awarded 0.134328358
## 58 Awarded 0.134328358 Awarded 0.268656716 Awarded 0.268656716
## 59 Awarded 0.074626866 Awarded 0.208955224 Awarded 0.208955224
## 60 Awarded 0.328358209 Awarded 0.447761194 Awarded 0.507462687
## 61 Awarded 0.731343284 Awarded 1.000000000 Awarded 1.000000000
## 62 Awarded 0.089552239 Awarded 0.089552239 Awarded 0.089552239
## 63 Awarded 1.044776119 Awarded 1.044776119 Awarded 1.044776119
## 64 Awarded 0.179104478 Awarded 0.268656716 Awarded 0.238805970
## 65 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 66 Not Awarded 0.000000000 Awarded 0.075471698 Not Awarded 0.000000000
## 67 Awarded 0.006289308 Awarded 0.044025157 Awarded 0.044025157
## 68 Awarded 1.006289308 Awarded 1.006289308 Awarded 1.006289308
## 69 Awarded 0.062893082 Awarded 0.125786164 Awarded 0.125786164
## 70 Awarded 0.050314465 Awarded 0.144654088 Awarded 0.144654088
## 71 Awarded 0.044025157 Awarded 1.000000000 Awarded 1.000000000
## 72 Awarded 0.125786164 Awarded 0.433962264 Awarded 0.433962264
## 73 Not Awarded 0.000000000 Awarded 0.188679245 Not Awarded 0.000000000
## 74 Not Awarded 0.000000000 Awarded 0.800000000 Not Awarded 0.000000000
## 75 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 76 Awarded 0.200000000 Awarded 0.200000000 Awarded 0.200000000
## 77 Awarded 0.400000000 Awarded 0.400000000 Awarded 0.400000000
## 78 Not Awarded 0.000000000 Awarded 0.800000000 Not Awarded 0.000000000
## 79 Awarded 0.121212121 Awarded 0.232323232 Awarded 0.232323232
## 80 Not Awarded 0.000000000 Awarded 0.494949495 Awarded 0.494949495
## 81 Not Awarded 0.000000000 Awarded 0.202020202 Awarded 0.202020202
## 82 Awarded 0.101010101 Awarded 0.808080808 Awarded 0.808080808
## 83 Awarded 1.020202020 Awarded 1.020202020 Awarded 1.020202020
## 84 Not Awarded 0.000000000 Awarded 0.204545455 Awarded 0.204545455
## 85 Not Awarded 0.000000000 Awarded 0.113636364 Awarded 0.113636364
## 86 Awarded 1.113636364 Awarded 1.000000000 Awarded 1.000000000
## 87 Not Awarded 0.000000000 Awarded 0.250000000 Awarded 0.250000000
## 88 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 89 Awarded 0.147058824 Not Awarded 0.000000000 Awarded 0.147058824
## 90 Not Awarded 0.000000000 Awarded 0.274509804 Awarded 0.274509804
## 91 Awarded 0.343137255 Awarded 0.392156863 Awarded 0.460784314
## 92 Awarded 0.065217391 Awarded 0.054347826 Awarded 0.065217391
## 93 Awarded 0.239130435 Awarded 0.304347826 Awarded 0.336956522
## 94 Awarded 1.010869565 Awarded 1.010869565 Awarded 1.010869565
## 95 Awarded 1.152380952 Awarded 1.152380952 Awarded 1.152380952
## 96 Not Awarded 0.000000000 Awarded 0.314285714 Not Awarded 0.000000000
## 97 Not Awarded 0.000000000 Awarded 0.133333333 Awarded 0.133333333
## 98 Not Awarded 0.000000000 Awarded 0.491666667 Awarded 0.491666667
## 99 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 100 Awarded 0.175000000 Awarded 0.133333333 Awarded 0.191666667
## 101 Not Awarded 0.000000000 Awarded 0.183333333 Awarded 0.183333333
## 102 Not Awarded 0.000000000 Awarded 0.508333333 Awarded 0.508333333
## 103 Awarded 0.187500000 Awarded 1.000000000 Awarded 1.000000000
## 104 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 105 Not Awarded 0.000000000 Awarded 0.640625000 Not Awarded 0.000000000
## 106 Awarded 0.343750000 Awarded 0.406250000 Awarded 0.406250000
## 107 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 108 Awarded 0.406250000 Awarded 0.859375000 Awarded 0.859375000
## 109 Awarded 0.390625000 Awarded 1.000000000 Awarded 1.000000000
## 110 Not Awarded 0.000000000 Awarded 0.296875000 Not Awarded 0.000000000
## 111 Awarded 0.328125000 Awarded 1.000000000 Awarded 1.000000000
## 112 Not Awarded 0.000000000 Awarded 0.531250000 Not Awarded 0.000000000
## 113 Awarded 0.093750000 Awarded 0.546875000 Awarded 0.546875000
## 114 Awarded 0.562500000 Awarded 0.578125000 Awarded 0.578125000
## 115 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 116 Not Awarded 0.000000000 Awarded 0.187500000 Not Awarded 0.000000000
## 117 Awarded 1.109375000 Awarded 1.109375000 Awarded 1.109375000
## 118 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 119 Not Awarded 0.000000000 Awarded 0.285714286 Not Awarded 0.000000000
## 120 Not Awarded 0.000000000 Awarded 0.428571429 Awarded 0.428571429
## 121 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 122 Not Awarded 0.000000000 Awarded 0.714285714 Awarded 0.714285714
## 123 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.214285714
## 124 Not Awarded 0.000000000 Awarded 0.357142857 Awarded 0.357142857
## 125 Awarded 27.687500000 Awarded 27.312500000 Awarded 27.250000000
## 126 Not Awarded 0.000000000 Awarded 0.812500000 Awarded 0.812500000
## 127 Awarded 0.060240964 Awarded 0.060240964 Awarded 0.060240964
## 128 Awarded 1.132530120 Awarded 1.132530120 Awarded 1.132530120
## 129 Awarded 0.048192771 Not Awarded 0.000000000 Awarded 0.048192771
## 130 Not Awarded 0.000000000 Awarded 0.192771084 Awarded 0.192771084
## 131 Awarded 0.036144578 Awarded 0.036144578 Awarded 0.036144578
## 132 Awarded 0.048192771 Not Awarded 0.000000000 Awarded 0.048192771
## 133 Not Awarded 0.000000000 Awarded 1.206896552 Not Awarded 0.000000000
## 134 Not Awarded 0.000000000 Awarded 0.632183908 Awarded 0.632183908
## 135 Not Awarded 0.000000000 Awarded 0.206896552 Awarded 0.206896552
## 136 Awarded 0.080459770 Awarded 0.103448276 Awarded 0.114942529
## 137 Not Awarded 0.000000000 Awarded 0.057471264 Awarded 0.057471264
## 138 Not Awarded 0.000000000 Awarded 0.011494253 Awarded 0.011494253
## 139 Not Awarded 0.000000000 Awarded 0.206896552 Awarded 0.206896552
## 140 Awarded 1.229885057 Awarded 1.229885057 Awarded 1.229885057
## 141 Not Awarded 0.000000000 Awarded 0.356321839 Awarded 0.356321839
## 142 Not Awarded 0.000000000 Awarded 0.459770115 Awarded 0.459770115
## 143 Awarded 0.304347826 Awarded 0.460869565 Awarded 0.478260870
## 144 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 145 Not Awarded 0.000000000 Awarded 0.643478261 Not Awarded 0.000000000
## 146 Not Awarded 0.000000000 Awarded 0.660869565 Awarded 0.660869565
## 147 Awarded 0.286956522 Awarded 0.365217391 Awarded 0.452173913
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## 465 Not Awarded 0.000000000 Awarded 0.459770115 Awarded 0.459770115
## 466 Awarded 0.304347826 Awarded 0.460869565 Awarded 0.478260870
## 467 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 468 Not Awarded 0.000000000 Awarded 0.643478261 Not Awarded 0.000000000
## 469 Not Awarded 0.000000000 Awarded 0.660869565 Awarded 0.660869565
## 470 Awarded 0.286956522 Awarded 0.365217391 Awarded 0.452173913
## 471 Not Awarded 0.000000000 Awarded 0.139130435 Awarded 0.139130435
## 472 Awarded 0.226086957 Awarded 0.721739130 Awarded 0.756521739
## 473 Awarded 0.146341463 Awarded 0.121951220 Awarded 0.158536585
## 474 Awarded 0.268292683 Awarded 0.597560976 Awarded 0.597560976
## 475 Awarded 0.109756098 Awarded 0.341463415 Awarded 0.353658537
## 476 Not Awarded 0.000000000 Awarded 1.012195122 Not Awarded 0.000000000
## 477 Awarded 0.048780488 Awarded 0.036585366 Awarded 0.048780488
## 478 Awarded 0.207317073 Awarded 0.341463415 Awarded 0.353658537
## 479 Awarded 1.012195122 Awarded 1.012195122 Awarded 1.012195122
## 480 Awarded 0.097560976 Awarded 0.146341463 Awarded 0.134146341
## 481 Not Awarded 0.000000000 Awarded 0.267857143 Awarded 0.267857143
## 482 Awarded 1.142857143 Awarded 1.142857143 Awarded 1.142857143
## 483 Not Awarded 0.000000000 Awarded 0.190000000 Awarded 0.190000000
## 484 Not Awarded 0.000000000 Awarded 0.660000000 Not Awarded 0.000000000
## 485 Not Awarded 0.000000000 Awarded 1.010000000 Not Awarded 0.000000000
## 486 Not Awarded 0.000000000 Awarded 0.280000000 Awarded 0.260000000
## 487 Awarded 0.340000000 Awarded 0.510000000 Awarded 0.520000000
## 488 Awarded 1.060000000 Awarded 1.060000000 Awarded 1.060000000
## 489 Awarded 0.450000000 Awarded 0.500000000 Awarded 0.500000000
## 490 Not Awarded 0.000000000 Awarded 0.140000000 Awarded 0.140000000
## 491 Not Awarded 0.000000000 Awarded 0.210000000 Awarded 0.210000000
## 492 Not Awarded 0.000000000 Awarded 0.188679245 Awarded 0.188679245
## 493 Not Awarded 0.000000000 Awarded 0.207547170 Awarded 0.207547170
## 494 Not Awarded 0.000000000 Awarded 0.113207547 Not Awarded 0.000000000
## 495 Awarded 1.094339623 Awarded 1.094339623 Awarded 1.094339623
## 496 Not Awarded 0.000000000 Awarded 0.021505376 Awarded 0.021505376
## 497 Not Awarded 0.000000000 Awarded 0.129032258 Awarded 0.129032258
## 498 Awarded 1.086021505 Awarded 1.086021505 Awarded 1.086021505
## 499 Not Awarded 0.000000000 Awarded 0.301075269 Awarded 0.301075269
## 500 Awarded 0.322580645 Awarded 0.956989247 Awarded 0.956989247
## 501 Not Awarded 0.000000000 Awarded 0.100000000 Awarded 0.100000000
## 502 Not Awarded 0.000000000 Awarded 0.800000000 Awarded 0.800000000
## 503 Not Awarded 0.000000000 Awarded 0.600000000 Awarded 0.600000000
## 504 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 505 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 506 Awarded 1.095238095 Awarded 1.095238095 Awarded 1.095238095
## 507 Awarded 0.571428571 Awarded 1.000000000 Awarded 1.000000000
## 508 Not Awarded 0.000000000 Awarded 0.030303030 Awarded 0.030303030
## 509 Not Awarded 0.000000000 Awarded 0.030303030 Awarded 0.030303030
## 510 Awarded 2.090909091 Awarded 2.090909091 Awarded 2.090909091
## 511 Awarded 2.588235294 Awarded 2.588235294 Awarded 2.588235294
## 512 Not Awarded 0.000000000 Awarded 0.411764706 Awarded 0.411764706
## 513 Not Awarded 0.000000000 Awarded 0.470588235 Awarded 0.470588235
## 514 Not Awarded 0.000000000 Awarded 0.419354839 Not Awarded 0.000000000
## 515 Awarded 1.032258065 Awarded 1.032258065 Awarded 1.032258065
## 516 Awarded 0.209677419 Awarded 0.225806452 Awarded 0.225806452
## 517 Not Awarded 0.000000000 Awarded 0.258064516 Awarded 0.258064516
## 518 Awarded 0.145161290 Awarded 0.225806452 Awarded 0.225806452
## 519 Not Awarded 0.000000000 Awarded 0.177419355 Awarded 0.177419355
## 520 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 521 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 522 Not Awarded 0.000000000 Awarded 0.443181818 Awarded 0.443181818
## 523 Awarded 0.125000000 Awarded 0.045454545 Awarded 0.136363636
## 524 Not Awarded 0.000000000 Awarded 0.250000000 Awarded 0.250000000
## 525 Not Awarded 0.000000000 Awarded 0.022727273 Awarded 0.022727273
## 526 Awarded 0.116883117 Awarded 0.259740260 Awarded 0.272727273
## 527 Not Awarded 0.000000000 Awarded 0.012987013 Not Awarded 0.000000000
## 528 Awarded 0.584415584 Awarded 0.766233766 Awarded 0.818181818
## 529 Not Awarded 0.000000000 Awarded 0.012987013 Not Awarded 0.000000000
## 530 Not Awarded 0.000000000 Awarded 0.207792208 Awarded 0.207792208
## 531 Not Awarded 0.000000000 Awarded 0.012987013 Not Awarded 0.000000000
## 532 Not Awarded 0.000000000 Awarded 0.272727273 Awarded 0.272727273
## 533 Awarded 1.454545455 Awarded 1.454545455 Awarded 1.454545455
## 534 Awarded 0.025974026 Not Awarded 0.000000000 Awarded 0.025974026
## 535 Awarded 0.350649351 Awarded 0.662337662 Awarded 0.662337662
## 536 Not Awarded 0.000000000 Awarded 0.138888889 Awarded 0.138888889
## 537 Not Awarded 0.000000000 Awarded 0.166666667 Awarded 0.166666667
## 538 Not Awarded 0.000000000 Awarded 0.055555556 Awarded 0.055555556
## 539 Not Awarded 0.000000000 Awarded 0.111111111 Awarded 0.111111111
## 540 Awarded 0.222222222 Awarded 0.555555556 Awarded 0.555555556
## 541 Not Awarded 0.000000000 Awarded 0.333333333 Awarded 0.333333333
## 542 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.305555556
## 543 Not Awarded 0.000000000 Awarded 0.388888889 Awarded 0.388888889
## 544 Not Awarded 0.000000000 Awarded 0.164179104 Awarded 0.164179104
## 545 Not Awarded 0.000000000 Awarded 1.014925373 Not Awarded 0.000000000
## 546 Awarded 0.119402985 Awarded 0.179104478 Awarded 0.194029851
## 547 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 548 Not Awarded 0.000000000 Awarded 0.164179104 Awarded 0.164179104
## 549 Not Awarded 0.000000000 Awarded 0.104477612 Not Awarded 0.000000000
## 550 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 551 Awarded 0.012820513 Not Awarded 0.000000000 Not Awarded 0.000000000
## 552 Awarded 0.435897436 Awarded 0.179487179 Awarded 0.435897436
## 553 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 554 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 555 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 556 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 557 Awarded 0.128205128 Awarded 0.384615385 Awarded 0.384615385
## 558 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 559 Not Awarded 0.000000000 Awarded 1.200000000 Not Awarded 0.000000000
## 560 Not Awarded 0.000000000 Awarded 1.000000000 Awarded 1.000000000
## 561 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 562 Awarded 0.400000000 Awarded 0.800000000 Awarded 0.800000000
## 563 Awarded 1.200000000 Awarded 1.400000000 Awarded 1.400000000
## 564 Not Awarded 0.000000000 Awarded 1.021739130 Not Awarded 0.000000000
## 565 Not Awarded 0.000000000 Awarded 1.021739130 Not Awarded 0.000000000
## 566 Awarded 0.173913043 Awarded 0.413043478 Awarded 0.413043478
## 567 Awarded 0.521739130 Awarded 0.760869565 Awarded 0.782608696
## 568 Not Awarded 0.000000000 Awarded 1.021739130 Not Awarded 0.000000000
## 569 Not Awarded 0.000000000 Awarded 1.021739130 Not Awarded 0.000000000
## 570 Awarded 0.521739130 Awarded 0.565217391 Awarded 0.565217391
## 571 Awarded 1.021739130 Awarded 1.021739130 Awarded 1.021739130
## 572 Awarded 0.015151515 Not Awarded 0.000000000 Awarded 0.015151515
## 573 Awarded 3.136363636 Awarded 1.166666667 Awarded 3.136363636
## 574 Awarded 0.242424242 Awarded 0.242424242 Awarded 0.242424242
## 575 Not Awarded 0.000000000 Awarded 0.090909091 Awarded 0.090909091
## 576 Not Awarded 0.000000000 Awarded 0.121212121 Awarded 0.121212121
## 577 Not Awarded 0.000000000 Awarded 0.212121212 Awarded 0.212121212
## 578 Not Awarded 0.000000000 Awarded 0.196969697 Awarded 0.196969697
## 579 Not Awarded 0.000000000 Awarded 0.409090909 Awarded 0.409090909
## 580 Not Awarded 0.000000000 Awarded 0.015151515 Not Awarded 0.000000000
## 581 Awarded 0.242424242 Awarded 0.924242424 Awarded 0.939393939
## 582 Awarded 0.010526316 Awarded 0.010526316 Awarded 0.010526316
## 583 Awarded 0.031578947 Awarded 0.063157895 Awarded 0.063157895
## 584 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 585 Not Awarded 0.000000000 Awarded 0.473684211 Awarded 0.473684211
## 586 Awarded 0.105263158 Not Awarded 0.000000000 Awarded 0.105263158
## 587 Awarded 0.042105263 Awarded 0.052631579 Awarded 0.052631579
## 588 Not Awarded 0.000000000 Awarded 0.242105263 Awarded 0.242105263
## 589 Awarded 0.507874016 Awarded 1.000000000 Awarded 1.000000000
## 590 Awarded 0.185039370 Awarded 0.433070866 Awarded 0.444881890
## 591 Not Awarded 0.000000000 Awarded 0.015748031 Awarded 0.015748031
## 592 Awarded 0.062992126 Awarded 0.102362205 Awarded 0.106299213
## 593 Awarded 0.094488189 Awarded 0.173228346 Awarded 0.169291339
## 594 Not Awarded 0.000000000 Awarded 0.141732283 Awarded 0.141732283
## 595 Not Awarded 0.000000000 Awarded 0.019685039 Awarded 0.019685039
## 596 Not Awarded 0.000000000 Awarded 0.125984252 Not Awarded 0.000000000
## 597 Awarded 0.051181102 Awarded 0.082677165 Awarded 0.082677165
## 598 Awarded 0.161417323 Awarded 0.208661417 Awarded 0.208661417
## 599 Not Awarded 0.000000000 Awarded 0.003937008 Awarded 0.003937008
## 600 Awarded 0.011811024 Not Awarded 0.000000000 Awarded 0.011811024
## 601 Awarded 0.062992126 Awarded 0.066929134 Awarded 0.082677165
## 602 Not Awarded 0.000000000 Awarded 0.200787402 Awarded 0.200787402
## 603 Awarded 1.011811024 Awarded 1.011811024 Awarded 1.011811024
## 604 Awarded 0.003937008 Awarded 0.003937008 Not Awarded 0.000000000
## 605 Awarded 1.241379310 Awarded 1.241379310 Awarded 1.241379310
## 606 Awarded 0.051851852 Awarded 0.066666667 Awarded 0.074074074
## 607 Not Awarded 0.000000000 Awarded 0.237037037 Awarded 0.237037037
## 608 Awarded 0.007407407 Awarded 0.214814815 Awarded 0.214814815
## 609 Awarded 1.022222222 Awarded 1.022222222 Awarded 1.022222222
## 610 Not Awarded 0.000000000 Awarded 0.511111111 Awarded 0.503703704
## 611 Not Awarded 0.000000000 Awarded 0.992592593 Not Awarded 0.000000000
## 612 Not Awarded 0.000000000 Awarded 0.714285714 Awarded 0.714285714
## 613 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 614 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 615 Not Awarded 0.000000000 Awarded 0.428571429 Awarded 0.428571429
## 616 Not Awarded 0.000000000 Awarded 0.461538462 Awarded 0.076923077
## 617 Not Awarded 0.000000000 Awarded 0.384615385 Awarded 0.384615385
## 618 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.717948718
## 619 Not Awarded 0.000000000 Awarded 0.410256410 Awarded 0.410256410
## 620 Not Awarded 0.000000000 Awarded 0.230769231 Awarded 0.230769231
## 621 Not Awarded 0.000000000 Awarded 0.282051282 Awarded 0.282051282
## 622 Not Awarded 0.000000000 Awarded 0.076923077 Not Awarded 0.000000000
## 623 Not Awarded 0.000000000 Awarded 0.384615385 Not Awarded 0.000000000
## 624 Not Awarded 0.000000000 Awarded 0.025641026 Not Awarded 0.000000000
## 625 Not Awarded 0.000000000 Awarded 0.076923077 Awarded 0.076923077
## 626 Awarded 0.102564103 Awarded 0.102564103 Awarded 0.102564103
## 627 Not Awarded 0.000000000 Awarded 0.055555556 Awarded 0.055555556
## 628 Awarded 0.125000000 Awarded 0.194444444 Awarded 0.194444444
## 629 Not Awarded 0.000000000 Awarded 0.138888889 Awarded 0.138888889
## 630 Awarded 1.166666667 Awarded 1.152777778 Awarded 1.166666667
## 631 Not Awarded 0.000000000 Awarded 0.111111111 Awarded 0.111111111
## 632 Not Awarded 0.000000000 Awarded 0.277777778 Awarded 0.277777778
## 633 Not Awarded 0.000000000 Awarded 0.083333333 Awarded 0.083333333
## 634 Not Awarded 0.000000000 Awarded 0.125000000 Awarded 0.125000000
## 635 Not Awarded 0.000000000 Awarded 1.000000000 Not Awarded 0.000000000
## 636 Awarded 0.218181818 Awarded 0.327272727 Awarded 0.327272727
## 637 Not Awarded 0.000000000 Awarded 0.145454545 Awarded 0.145454545
## 638 Not Awarded 0.000000000 Awarded 0.381818182 Awarded 0.381818182
## 639 Not Awarded 0.000000000 Awarded 0.327272727 Awarded 0.327272727
## 640 Awarded 0.054545455 Awarded 0.054545455 Awarded 0.054545455
## 641 Awarded 1.000000000 Awarded 1.000000000 Awarded 1.000000000
## 642 Awarded 0.363636364 Awarded 0.854545455 Awarded 0.854545455
## 643 Awarded 0.109090909 Awarded 0.200000000 Awarded 0.200000000
## 644 Not Awarded 0.000000000 Awarded 0.581818182 Awarded 0.581818182
## 645 Awarded 1.043478261 Awarded 1.043478261 Awarded 1.043478261
## 646 Awarded 0.086956522 Awarded 0.173913043 Awarded 0.173913043
## TotalAgencies FEMACSAvg FundType funds
## 1 10 0.9766411 ReqAmt 2434844.857
## 2 8 0.7550000 ReqAmt 19863078.852
## 3 6 1.0000000 ReqAmt 5197162.960
## 4 1 1.0000000 ReqAmt 1846.870
## 5 10 0.9551913 ReqAmt 35717008.133
## 6 2 1.0000000 ReqAmt 201660.260
## 7 4 1.0000000 ReqAmt 957840.990
## 8 2 1.0000000 ReqAmt 113969.080
## 9 2 1.0000000 ReqAmt 637446.550
## 10 5 1.0000000 ReqAmt 308420.770
## 11 2 1.0000000 ReqAmt 16978.890
## 12 4 1.0000000 ReqAmt 639155.790
## 13 1 1.0000000 ReqAmt 113021.520
## 14 5 0.9391026 ReqAmt 8963656.662
## 15 3 1.0000000 ReqAmt 1207244.700
## 16 3 1.0000000 ReqAmt 25315.020
## 17 8 0.8936012 ReqAmt 3529626.690
## 18 9 0.9198390 ReqAmt 162666766.716
## 19 4 0.9062500 ReqAmt 644115.000
## 20 17 0.9559613 ReqAmt 412969248.115
## 21 13 0.9871795 ReqAmt 73939.550
## 22 9 1.0000000 ReqAmt 111265.000
## 23 1 1.0000000 ReqAmt 315991.420
## 24 14 0.9341270 ReqAmt 38242.252
## 25 8 0.9285714 ReqAmt 6053446.853
## 26 1 1.0000000 ReqAmt 64018.400
## 27 16 1.0000000 ReqAmt 210464.360
## 28 8 0.9816176 ReqAmt 58462371.498
## 29 17 0.9658304 ReqAmt 28294846.186
## 30 19 0.9164789 ReqAmt 53400370.670
## 31 8 0.9513889 ReqAmt 8672055.556
## 32 10 1.0000000 ReqAmt 42664.260
## 33 2 1.0000000 ReqAmt 216446.440
## 34 9 1.0000000 ReqAmt 9933.690
## 35 9 0.9715414 ReqAmt 4132990.190
## 36 17 0.9628422 ReqAmt 670111993.343
## 37 12 0.9599868 ReqAmt 7207299.594
## 38 2 1.0000000 ReqAmt 1721710.130
## 39 3 1.0000000 ReqAmt 221173.140
## 40 4 1.0000000 ReqAmt 3653.860
## 41 13 0.9331854 ReqAmt 80554165.268
## 42 11 0.9738636 ReqAmt 555593.710
## 43 19 0.9717086 ReqAmt 8533396.606
## 44 1 0.7500000 ReqAmt 1130890.200
## 45 3 0.8272198 ReqAmt 33230911.308
## 46 15 0.9916667 ReqAmt 1280811.180
## 47 2 1.0000000 ReqAmt 132717.390
## 48 2 1.0000000 ReqAmt 15267.200
## 49 15 0.9958333 ReqAmt 1306839.321
## 50 4 0.7500000 ReqAmt 24163037.250
## 51 15 1.0000000 ReqAmt 1388233.930
## 52 9 0.9537701 ReqAmt 29138452.633
## 53 5 0.8333333 ReqAmt 810984.676
## 54 3 0.9444444 ReqAmt 241737.035
## 55 7 0.9553571 ReqAmt 2232723.088
## 56 3 1.0000000 ReqAmt 2666.200
## 57 1 1.0000000 ReqAmt 5402.120
## 58 4 0.8750000 ReqAmt 1374983.335
## 59 5 0.9500000 ReqAmt 107678.230
## 60 4 1.0000000 ReqAmt 15134.030
## 61 25 0.9681893 ReqAmt 206435753.579
## 62 2 1.0000000 ReqAmt 24006.000
## 63 6 0.9045940 ReqAmt 212878073.490
## 64 22 0.9575652 ReqAmt 65488854.663
## 65 17 0.9440368 ReqAmt 2451923.077
## 66 1 1.0000000 ReqAmt 15517.410
## 67 2 1.0000000 ReqAmt 105956.560
## 68 3 0.8622449 ReqAmt 125561025.953
## 69 8 1.0000000 ReqAmt 953479.950
## 70 1 1.0000000 ReqAmt 297671.400
## 71 22 0.9963145 ReqAmt 5763674.354
## 72 8 0.9719388 ReqAmt 128634877.768
## 73 11 0.9393939 ReqAmt 307161.315
## 74 15 0.9877778 ReqAmt 1654630.318
## 75 9 0.9081197 ReqAmt 229140190.814
## 76 17 0.9416620 ReqAmt 4008969.074
## 77 1 1.0000000 ReqAmt 102651.430
## 78 11 1.0000000 ReqAmt 443989.310
## 79 2 0.8750000 ReqAmt 272460.020
## 80 3 1.0000000 ReqAmt 1108201.320
## 81 2 1.0000000 ReqAmt 104938.200
## 82 10 0.9485833 ReqAmt 4512388.537
## 83 4 0.9024621 ReqAmt 37976680.957
## 84 1 1.0000000 ReqAmt 286.360
## 85 1 1.0000000 ReqAmt 2653.770
## 86 8 0.8577398 ReqAmt 50743081.963
## 87 1 1.0000000 ReqAmt 1182.960
## 88 29 0.9718870 ReqAmt 316300288.755
## 89 1 1.0000000 ReqAmt 2723.090
## 90 3 1.0000000 ReqAmt 2086165.980
## 91 2 1.0000000 ReqAmt 180637.230
## 92 1 1.0000000 ReqAmt 29018.400
## 93 2 1.0000000 ReqAmt 271707.530
## 94 5 0.9577160 ReqAmt 208865661.603
## 95 5 0.8254386 ReqAmt 35062682.416
## 96 2 0.8750000 ReqAmt 45199.790
## 97 1 1.0000000 ReqAmt 6603.170
## 98 1 1.0000000 ReqAmt 232262.580
## 99 7 0.9467419 ReqAmt 45226383.104
## 100 2 1.0000000 ReqAmt 46008.880
## 101 1 1.0000000 ReqAmt 1018002.830
## 102 3 1.0000000 ReqAmt 687623.480
## 103 12 0.9692460 ReqAmt 3512938.729
## 104 11 1.0000000 ReqAmt 280939.450
## 105 3 1.0000000 ReqAmt 211961.670
## 106 17 0.9526612 ReqAmt 87709869.791
## 107 7 0.9821429 ReqAmt 1008.840
## 108 20 0.9642708 ReqAmt 10618838.020
## 109 32 0.9880528 ReqAmt 395544734.495
## 110 2 1.0000000 ReqAmt 18783.640
## 111 22 0.8891936 ReqAmt 88887842.222
## 112 10 1.0000000 ReqAmt 86335.010
## 113 10 1.0000000 ReqAmt 424387.650
## 114 12 1.0000000 ReqAmt 5771336.070
## 115 3 1.0000000 ReqAmt 509269.170
## 116 5 0.9375000 ReqAmt 1149.980
## 117 7 0.8925463 ReqAmt 161422456.144
## 118 12 0.9670139 ReqAmt 1713.930
## 119 4 1.0000000 ReqAmt 2425.580
## 120 2 1.0000000 ReqAmt 236987.220
## 121 3 1.0000000 ReqAmt 524921.740
## 122 3 1.0000000 ReqAmt 119947.680
## 123 11 0.9589371 ReqAmt 96529491.803
## 124 2 1.0000000 ReqAmt 260036.530
## 125 12 0.9625000 ReqAmt 12554411.200
## 126 2 1.0000000 ReqAmt 134966.980
## 127 2 0.8750000 ReqAmt 82661.270
## 128 11 0.9412578 ReqAmt 146321455.050
## 129 1 1.0000000 ReqAmt 346200.000
## 130 1 1.0000000 ReqAmt 1168689.180
## 131 3 0.9166667 ReqAmt 352250.492
## 132 2 1.0000000 ReqAmt 103137.600
## 133 1 0.7500000 ReqAmt 1341.345
## 134 3 1.0000000 ReqAmt 2289982.840
## 135 2 1.0000000 ReqAmt 1163854.640
## 136 1 1.0000000 ReqAmt 5290.460
## 137 1 1.0000000 ReqAmt 387650.630
## 138 1 1.0000000 ReqAmt 25500.000
## 139 4 1.0000000 ReqAmt 24968.320
## 140 7 0.8838037 ReqAmt 42212935.796
## 141 4 1.0000000 ReqAmt 1470774.280
## 142 1 1.0000000 ReqAmt 1949.950
## 143 5 1.0000000 ReqAmt 835516.430
## 144 14 0.9339262 ReqAmt 121502081.226
## 145 4 0.9250000 ReqAmt 2532262.503
## 146 2 1.0000000 ReqAmt 283095.560
## 147 4 0.9000000 ReqAmt 625362.566
## 148 2 0.9375000 ReqAmt 790951.948
## 149 8 0.9722222 ReqAmt 1234320.114
## 150 3 0.9583333 ReqAmt 415740.236
## 151 7 0.9785714 ReqAmt 1101231.112
## 152 3 1.0000000 ReqAmt 254743.430
## 153 2 1.0000000 ReqAmt 273892.820
## 154 1 1.0000000 ReqAmt 3814.350
## 155 1 1.0000000 ReqAmt 308702.950
## 156 5 0.8455278 ReqAmt 217418030.303
## 157 1 1.0000000 ReqAmt 118977.370
## 158 1 1.0000000 ReqAmt 78.240
## 159 6 0.8928571 ReqAmt 32247036.357
## 160 1 1.0000000 ReqAmt 364942.000
## 161 3 0.9583333 ReqAmt 1140454.008
## 162 3 1.0000000 ReqAmt 154445.340
## 163 4 1.0000000 ReqAmt 666950.560
## 164 21 0.9570023 ReqAmt 51944911.988
## 165 9 0.9176731 ReqAmt 113207921.345
## 166 12 0.9849850 ReqAmt 7120007.749
## 167 2 1.0000000 ReqAmt 263050.590
## 168 2 1.0000000 ReqAmt 90192.220
## 169 1 1.0000000 ReqAmt 6202.280
## 170 2 1.0000000 ReqAmt 19415.820
## 171 8 0.9427083 ReqAmt 67117.355
## 172 3 0.7865497 ReqAmt 49769771.017
## 173 1 1.0000000 ReqAmt 50799.880
## 174 1 1.0000000 ReqAmt 77921.730
## 175 3 0.8345960 ReqAmt 29947514.345
## 176 1 1.0000000 ReqAmt 3676.840
## 177 11 0.9576923 ReqAmt 10644894.488
## 178 1 1.0000000 ReqAmt 20544.030
## 179 2 1.0000000 ReqAmt 22788.670
## 180 2 1.0000000 ReqAmt 265274.050
## 181 4 0.7834154 ReqAmt 21206585.948
## 182 28 0.9777722 ReqAmt 24127007.028
## 183 22 0.9735742 ReqAmt 155275737.024
## 184 8 0.9843750 ReqAmt 4571346.350
## 185 1 0.9117647 ReqAmt 79832.021
## 186 4 1.0000000 ReqAmt 273022.050
## 187 8 0.8347631 ReqAmt 65519432.546
## 188 15 0.9623679 ReqAmt 108482263.892
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## 505 28 0.9777722 OblAmt 33454539.256
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## 588 1 1.0000000 OblAmt 154605.100
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## 594 1 1.0000000 OblAmt 736916.440
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## 644 4 1.0000000 OblAmt 134885.370
## 645 5 0.9250000 OblAmt 8636910.953
## 646 3 1.0000000 OblAmt 188671.410#change labels
levels(data_long$FundType) <- c("Requested Amount", "Obligated Amount")
#now usual ggplot2 density plot
funds_density <- ggplot2::ggplot(data = data_long, aes(x = log(funds), color = FundType, fill = FundType)) +
geom_density(alpha = 0.4) +
facet_wrap(~ FundType) +
scale_fill_manual(values = c("#6ece58", "#26828e")) +
scale_color_manual(values = c("#6ece58", "#26828e")) +
labs(x = "Log(Funding)",
y = "Density") +
theme(legend.position="none")
funds_density
#save file
funds_density_fig_file = here("results","funds_density.png")
ggsave(filename = funds_density_fig_file, plot = funds_density)## Saving 7 x 5 in imagePredictor: State
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$state, report.nas = FALSE)## Frequencies
## EDAdata_outliers$state
## Label: State Abbreviation
## Type: Character
##
## Freq % % Cum.
## ----------- ------ -------- --------
## AK 4 1.24 1.24
## AL 4 1.24 2.48
## AR 8 2.48 4.95
## AZ 3 0.93 5.88
## CA 19 5.88 11.76
## CO 5 1.55 13.31
## CT 6 1.86 15.17
## DC 2 0.62 15.79
## DE 1 0.31 16.10
## FL 13 4.02 20.12
## GA 8 2.48 22.60
## HI 5 1.55 24.15
## IA 5 1.55 25.70
## ID 4 1.24 26.93
## IL 4 1.24 28.17
## IN 3 0.93 29.10
## KS 3 0.93 30.03
## KY 5 1.55 31.58
## LA 16 4.95 36.53
## MA 6 1.86 38.39
## ME 2 0.62 39.01
## MI 6 1.86 40.87
## MN 10 3.10 43.96
## MO 7 2.17 46.13
## MS 8 2.48 48.61
## MT 2 0.62 49.23
## NC 9 2.79 52.01
## ND 4 1.24 53.25
## NE 5 1.55 54.80
## NH 4 1.24 56.04
## NJ 3 0.93 56.97
## NM 3 0.93 57.89
## NV 3 0.93 58.82
## NY 6 1.86 60.68
## OH 6 1.86 62.54
## OK 10 3.10 65.63
## OR 8 2.48 68.11
## PA 6 1.86 69.97
## PR 9 2.79 72.76
## RI 5 1.55 74.30
## SC 8 2.48 76.78
## SD 10 3.10 79.88
## TN 7 2.17 82.04
## TX 16 4.95 87.00
## UT 1 0.31 87.31
## VA 6 1.86 89.16
## VT 4 1.24 90.40
## WA 11 3.41 93.81
## WI 8 2.48 96.28
## WV 10 3.10 99.38
## WY 2 0.62 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#filter top 5 states
#start with obligated amount
obl_fig_state <- EDAdata_outliers %>%
dplyr::add_count(state) %>%
dplyr::filter(dplyr::dense_rank(-n) < 5) %>%
ggplot2::ggplot(aes(x = state, y = OblAmt)) +
geom_boxplot(color = "#440154") +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
labs(x = "State or Territory",
y = "Obligated FEMA Funds")
obl_fig_state
#repeat for requested funds
req_fig_state <- EDAdata_outliers %>%
dplyr::add_count(state) %>%
dplyr::filter(dplyr::dense_rank(-n) < 6) %>%
ggplot2::ggplot(aes(x = state, y = ReqAmt)) +
geom_boxplot(color = "#440154") +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
labs(x = " " ,
y = "Requested FEMA Funds")
req_fig_state
#combine into one plot
plot_states <- cowplot::plot_grid(req_fig_state,
obl_fig_state,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_states
title_state <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds For Five Most Disaster Prone States",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_states_funds <- cowplot::plot_grid(
title_state, plot_states,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_states_funds
#save file
states_fig_file = here("results","states-funds.png")
ggsave(filename = states_fig_file, plot = plot_states_funds)## Saving 7 x 5 in image#create a table where columns represent requested and obligated funds and rows are 5 most disaster-prone states
decs_state_5 <- EDAdata_outliers %>%
dplyr::add_count(state) %>%
dplyr::filter(dense_rank(-n) < 6) %>%
gtsummary::select(state, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = state,
label = list(state ~ "State or Territory",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3", "stat_4", "stat_5") ~ "**State**") %>%
gtsummary::modify_caption("**Table 4. FEMA Funding for 5 Most Disaster-Prone States 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_state_5| Variable | State | SD, N = 101 | TX, N = 161 | WA, N = 111 | WV, N = 101 | ||||
|---|---|---|---|---|---|---|---|---|---|
| CA, N = 191 | FL, N = 131 | LA, N = 161 | MN, N = 101 | OK, N = 101 | |||||
| Requested Funds | 65,900,489 (173,898,625) | 37,851,532 (78,320,708) | 47,187,723 (104,127,805) | 4,758,425 (13,184,247) | 6,413,888 (14,086,311) | 2,727,818 (7,026,623) | 48,019,502 (135,429,624) | 14,599,599 (47,096,442) | 10,655,340 (32,155,687) |
| Obligated Funds | 65,900,489 (173,898,625) | 37,851,532 (78,320,708) | 47,187,723 (104,127,805) | 4,758,425 (13,184,247) | 6,413,888 (14,086,311) | 2,727,818 (7,026,623) | 48,087,226 (135,451,571) | 14,599,599 (47,096,442) | 10,656,415 (32,155,346) |
|
1
Mean (SD)
|
|||||||||
#save file
decs_state_file = here("results", "decs-state-5.Rds")
saveRDS(decs_state_5, file = decs_state_file)Predictor: Incident Year
#summary statistics since it's continuous
base::summary(EDAdata_outliers$IncidentYear)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2012 2015 2017 2017 2020 2021#examine graphical relationship with outcomes
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = IncidentYear)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = IncidentYear)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$IncidentYear)
## [1] 4 40#histogram distribution
year_hist <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x=IncidentYear)) +
geom_bar(fill="#482878") +
scale_x_continuous(breaks = seq(2012, 2021, by = 1)) +
scale_y_continuous(expand = c(0, 0), limits = c(0, 125)) +
geom_text(stat='count', aes(label=..count..), vjust=-1) +
labs(x = "Incident Year",
y = "Number of Declarations",
title = "Disaster Declarations per Year (2012 - 2021)")
year_hist
#save file
year_fig_file = here("results","incidentyear.png")
ggsave(filename = year_fig_file, plot = year_hist)## Saving 7 x 5 in image#examine graphical relationship with outcomes of interest
#look at total cost per incident year
req_fig_year <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = IncidentYear)) +
geom_bar(aes(y = ReqAmt),
stat = "identity",
fill = "#6ece58") +
scale_y_continuous(expand = c(0, 0),
labels = scales::dollar_format()) +
scale_x_continuous(breaks = seq(2012, 2021, by = 1)) +
labs(x = "Incident Year",
y = "Funds ($)",
subtitle = "Requested")
req_fig_year
#repeat for obligated funds
obl_fig_year <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = IncidentYear)) +
geom_bar(aes(y = OblAmt),
stat = "identity",
fill = "#26828e") +
scale_y_continuous(expand = c(0, 0),
labels = scales::dollar_format()) +
scale_x_continuous(breaks = seq(2012, 2021, by = 1)) +
labs(x = "Incident Year",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_year
#combine into one plot
plot_col <- cowplot::plot_grid(req_fig_year,
obl_fig_year,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_col
title1 <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds Per Year",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_year <- cowplot::plot_grid(
title1, plot_col,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
funds_fig_year
#save file
funds_year_fig_file = here("results","funds-years.png")
ggsave(filename = funds_year_fig_file, plot = funds_fig_year)## Saving 7 x 5 in image#create a table where columns represent requested and obligated funds and rows are 5 most disaster-prone states
decs_year_3 <- EDAdata_outliers %>%
dplyr::add_count(IncidentYear) %>%
dplyr::filter(dense_rank(-n) < 4) %>%
gtsummary::select(IncidentYear, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = IncidentYear,
label = list(IncidentYear ~ "Year",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Year**") %>%
gtsummary::modify_caption("**Table 2. FEMA Funding for the 3 Costliest Years (2012 - 2021)**") %>%
gtsummary::bold_labels()
decs_year_3| Variable | Year | ||
|---|---|---|---|
| 2012, N = 331 | 2017, N = 361 | 2020, N = 851 | |
| Requested Funds | 11,106,321 (55,959,108) | 155,310,630 (735,733,161) | 74,188,913 (115,267,136) |
| Obligated Funds | 11,388,973 (56,050,414) | 155,887,178 (739,000,529) | 74,194,235 (115,269,357) |
|
1
Mean (SD)
|
|||
#save file
decs_year_file = here("results", "decs-year-3.Rds")
saveRDS(decs_year_3, file = decs_year_file)Incident Month
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$IncidentMonth, report.nas = FALSE)## Frequencies
## EDAdata_outliers$IncidentMonth
## Label: Declaration Month
## Type: Ordered Factor
##
## Freq % % Cum.
## --------------- ------ -------- --------
## January 71 21.98 21.98
## February 22 6.81 28.79
## March 22 6.81 35.60
## April 24 7.43 43.03
## May 22 6.81 49.85
## June 25 7.74 57.59
## July 13 4.02 61.61
## August 31 9.60 71.21
## September 30 9.29 80.50
## October 41 12.69 93.19
## November 8 2.48 95.67
## December 14 4.33 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#filter top 5 states
#start with obligated amount
obl_fig_month <- EDAdata_outliers %>%
dplyr::add_count(state) %>%
dplyr::filter(dplyr::dense_rank(-n) < 5) %>%
ggplot2::ggplot(aes(x = IncidentMonth, y = OblAmt)) +
geom_boxplot(color = "#440154") +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
labs(x = "Incident Month",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_month
#repeat for requested funds
req_fig_month <- EDAdata_outliers %>%
dplyr::add_count(state) %>%
dplyr::filter(dplyr::dense_rank(-n) < 6) %>%
ggplot2::ggplot(aes(x = IncidentMonth, y = ReqAmt)) +
geom_boxplot(color = "#440154") +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
labs(x = " " ,
y = "Funds ($)",
subtitle = "Requested")
req_fig_month
#combine into one plot
plot_month <- cowplot::plot_grid(req_fig_month,
obl_fig_month,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_month
title_month <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds By Month",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_month_funds <- cowplot::plot_grid(
title_month, plot_month,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_month_funds
#save file
month_fig_file = here("results","month-funds.png")
ggsave(filename = month_fig_file, plot = plot_month_funds)## Saving 7 x 5 in image#create a table where columns represent requested and obligated funds and rows are 5 most disaster-prone states
decs_month_5 <- EDAdata_outliers %>%
dplyr::add_count(IncidentMonth) %>%
dplyr::filter(dense_rank(-n) < 6) %>%
gtsummary::select(IncidentMonth, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = IncidentMonth,
label = list(IncidentMonth ~ "Incident Month",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3", "stat_4", "stat_5") ~ "**Incident Month**") %>%
gtsummary::modify_caption("**Table 4. FEMA Funding by Month 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_month_5| Variable | Incident Month | June, N = 251 | July, N = 01 | August, N = 311 | September, N = 301 | October, N = 411 | November, N = 01 | December, N = 01 | ||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| January, N = 711 | February, N = 01 | March, N = 01 | April, N = 01 | May, N = 01 | ||||||||
| Requested Funds | 86,384,351 (122,106,804) | NA (NA) | NA (NA) | NA (NA) | NA (NA) | 306,004 (458,082) | NA (NA) | 28,337,301 (83,202,807) | 157,997,671 (799,948,320) | 30,303,760 (115,811,181) | NA (NA) | NA (NA) |
| Obligated Funds | 86,397,557 (122,103,801) | NA (NA) | NA (NA) | NA (NA) | NA (NA) | 306,434 (459,213) | NA (NA) | 28,349,776 (83,236,236) | 158,657,132 (803,575,067) | 30,531,625 (115,807,818) | NA (NA) | NA (NA) |
|
1
Mean (SD)
|
||||||||||||
#save file
decs_month_file = here("results", "decs-month-5.Rds")
saveRDS(decs_month_5, file = decs_month_file)Predictor: Declaration Type
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$declarationType, report.nas = FALSE)## Frequencies
## EDAdata_outliers$declarationType
## Label: Declaration Type
## Type: Character
##
## Freq % % Cum.
## ----------- ------ -------- --------
## DR 260 80.50 80.50
## EM 63 19.50 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_DT <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = declarationType, y = OblAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Major Disaster Declaration", "Emergency Declaration")) +
labs(x = "Declaration Type",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_DT
#repeat for requested funds
req_fig_DT <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = declarationType, y = ReqAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Major Disaster Declaration", "Emergency Declaration")) +
labs(x = " ",
y = "Funds ($)",
subtitle = "Requested")
req_fig_DT
#combine into one plot
plot_DT <- cowplot::plot_grid(req_fig_DT,
obl_fig_DT,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_DT
title_DT <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds By Declaration Type",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_DT_funds <- cowplot::plot_grid(
title_DT, plot_DT,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_DT_funds
#save file
DT_fig_file = here("results","DT-funds.png")
ggsave(filename = DT_fig_file, plot = plot_DT_funds)## Saving 7 x 5 in image#create a table where rows represent requested and obligated funds and columns are declaration type
decs_dtype <- EDAdata_outliers %>%
gtsummary::select(declarationType, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = declarationType,
label = list(declarationType ~ "Type",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_header(update = list(
stat_1 ~ "**Major Disaster**",
stat_2 ~ "**Emergency**")) %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Type**") %>%
gtsummary::modify_caption("**Table 4. FEMA Funding for Declaration Types 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_dtype| Variable | Type | |
|---|---|---|
| Major Disaster1 | Emergency1 | |
| Requested Funds | 50,782,764 (284,903,397) | 518,576 (752,681) |
| Obligated Funds | 50,903,565 (286,070,333) | 518,576 (752,681) |
|
1
Mean (SD)
|
||
#save file
decs_dtype_file = here("results", "decs-DT.Rds")
saveRDS(decs_dtype, file = decs_dtype_file)Predictor: Incident Type
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$incidentType, report.nas = FALSE)## Frequencies
## EDAdata_outliers$incidentType
## Label: Incident Type
## Type: Character
##
## Freq % % Cum.
## ---------------------- ------ -------- --------
## Biological 60 18.58 18.58
## Earthquake 4 1.24 19.81
## Fire 20 6.19 26.01
## Flood 61 18.89 44.89
## Hurricane 71 21.98 66.87
## Other 14 4.33 71.21
## Severe Ice Storm 4 1.24 72.45
## Severe Storm(s) 82 25.39 97.83
## Tornado 7 2.17 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_IT <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = incidentType, y = OblAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
labs(x = "Incident Type",
y = "Funds ($)",
subtitle = "Obligated") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
obl_fig_IT
#repeat for requested funds
req_fig_IT <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = incidentType, y = ReqAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
labs(x = " ",
y = "Funds ($)",
subtitle = "Requested") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
req_fig_IT
#combine into one plot
plot_IT <- cowplot::plot_grid(req_fig_IT,
obl_fig_IT,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_IT
title_IT <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds By Incident Type",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_IT_funds <- cowplot::plot_grid(
title_IT, plot_IT,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_IT_funds
#save file
IT_fig_file = here("results","IT-funds.png")
ggsave(filename = IT_fig_file, plot = plot_IT_funds)## Saving 7 x 5 in image#create a table where rows represent requested and obligated funds and columns are declaration type
decs_itype3 <- EDAdata %>%
dplyr::add_count(incidentType) %>%
dplyr::filter(dense_rank(-n) < 4) %>%
gtsummary::select(incidentType, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = incidentType,
label = list(incidentType ~ "Type",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2", "stat_3") ~ "**Type**") %>%
gtsummary::modify_caption("**Table 4. FEMA Funding for Declaration Types 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_itype3| Variable | Type | ||
|---|---|---|---|
| Flood, N = 611 | Hurricane, N = 731 | Severe Storm(s), N = 821 | |
| Requested Funds | 2,676,879 (11,264,643) | 82,137,002 (515,877,545) | 412,896 (1,056,124) |
| Obligated Funds | 2,691,729 (11,268,277) | 82,541,085 (518,171,447) | 413,710 (1,057,092) |
|
1
Mean (SD)
|
|||
#save file
decs_itype3_file = here("results", "decs-IT-3.Rds")
saveRDS(decs_itype3, file = decs_itype3_file)Predictor: Incident Duration
#convert class to numeric
EDAdata_outliers$IncidentDuration <- as.numeric(EDAdata_outliers$IncidentDuration, units="days")
#summary statistics since it's continuous
base::summary(EDAdata_outliers$IncidentDuration)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 5.0 14.0 136.9 47.5 660.0#NAs are ongoing events
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = IncidentDuration)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = IncidentDuration)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$IncidentDuration)
## [1] 2 5#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_dur <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = IncidentDuration)) +
geom_point(aes(y = ReqAmt),
color = "#6ece58",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Incident Duration (Days)",
y = "Funds ($)",
subtitle = "Requested")
req_fig_dur## Warning: Removed 1 rows containing missing values (geom_point).
#repeat for obligated funds
obl_fig_dur <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = IncidentDuration)) +
geom_point(aes(y = OblAmt),
color = "#26828e",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Incident Duration (Days)",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_dur## Warning: Removed 1 rows containing missing values (geom_point).
#combine into one plot
plot_col2 <- cowplot::plot_grid(req_fig_dur,
obl_fig_dur,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).plot_col2
title3 <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds by Incident Duration (Under $1B)",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_dur <- cowplot::plot_grid(
title3, plot_col2,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
funds_fig_dur
#save file
funds_dur_fig_file = here("results","funds-dur.png")
ggsave(filename = funds_dur_fig_file, plot = funds_fig_dur)## Saving 7 x 5 in imagePredictor: Population
#convert class to numeric
EDAdata_outliers$Population <- as.numeric(gsub(",", "", EDAdata_outliers$Population))
#summary statistics since it's continuous
base::summary(EDAdata_outliers$Population)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 576851 3011524 5706494 9242866 10439388 39538223#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = Population)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = Population)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$Population)
## [1] 20 21#examine graphical relationship with outcomes of interest
#look at total cost per pop
req_fig_pop <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = Population)) +
geom_point(aes(y = ReqAmt),
color = "#6ece58",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
scale_x_continuous(label = comma,
limits = c(0, 40000000)) +
labs(x = "State Population",
y = "Funds ($)",
subtitle = "Requested")
req_fig_pop## Warning: Removed 1 rows containing missing values (geom_point).
#repeat for obligated funds
obl_fig_pop <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = Population)) +
geom_point(aes(y = OblAmt),
color = "#26828e",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
scale_x_continuous(label = comma,
limits = c(0, 40000000)) +
labs(x = "State Population",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_pop## Warning: Removed 1 rows containing missing values (geom_point).
#combine into one plot
plot_col3 <- cowplot::plot_grid(req_fig_pop,
obl_fig_pop,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).plot_col3
title4 <- cowplot::ggdraw() +
draw_label(
"Requested vs obligated FEMA funds by State Population (Under $1B)",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_pop <- cowplot::plot_grid(
title4, plot_col3,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
funds_fig_pop
#save file
funds_pop_fig_file = here("results","funds-pop.png")
ggsave(filename = funds_pop_fig_file, plot = funds_fig_pop)## Saving 7 x 5 in imagePredictor: Total Number of Agencies Involved
#summary statistics since it's continuous
base::summary(EDAdata_outliers$TotalAgencies)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 6.245 8.000 55.000#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = TotalAgencies)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = TotalAgencies)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$TotalAgencies)
## [1] 233 275#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_ag <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = TotalAgencies)) +
geom_point(aes(y = ReqAmt),
color = "#6ece58",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Number of Federal Agencies",
y = "Funds ($)",
subtitle = "Requested")
req_fig_ag## Warning: Removed 1 rows containing missing values (geom_point).
#repeat for obligated funds
obl_fig_ag <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = TotalAgencies)) +
geom_point(aes(y = OblAmt),
color = "#26828e",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Number of Federal Agencies",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_ag## Warning: Removed 1 rows containing missing values (geom_point).
#combine into one plot
plot_col4 <- cowplot::plot_grid(req_fig_ag,
obl_fig_ag,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).plot_col4
title5 <- cowplot::ggdraw() +
draw_label(
"Requested vs obligated FEMA funds by number of federal agencies (Under $1B)",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_ag <- cowplot::plot_grid(
title5, plot_col4,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
funds_fig_ag
#save file
funds_ag_fig_file = here("results","funds-ag.png")
ggsave(filename = funds_ag_fig_file, plot = funds_fig_ag)## Saving 7 x 5 in imagePredictor: Number of Counties
#summary statistics since it's continuous
base::summary(EDAdata_outliers$Counties)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 46.00 67.00 74.96 88.00 254.00#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = Counties)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = Counties)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$Counties)
## [1] 266 267#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_count <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = Counties)) +
geom_point(aes(y = ReqAmt),
color = "#6ece58",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Number of Counties",
y = "Funds ($)",
subtitle = "Requested")
req_fig_count## Warning: Removed 1 rows containing missing values (geom_point).
#repeat for obligated funds
obl_fig_count <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = Counties)) +
geom_point(aes(y = OblAmt),
color = "#26828e",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Number of Counties",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_count## Warning: Removed 1 rows containing missing values (geom_point).
#combine into one plot
plot_col5 <- cowplot::plot_grid(req_fig_count,
obl_fig_count,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).plot_col5
title6 <- cowplot::ggdraw() +
draw_label(
"Requested vs obligated FEMA funds by number of counties (Under $1B)",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_count <- cowplot::plot_grid(
title6, plot_col5,
ncol = 1,
rel_heights = c(0.1, 1))
funds_fig_count
#save file
funds_count_fig_file = here("results","funds-count.png")
ggsave(filename = funds_count_fig_file, plot = funds_fig_count)## Saving 7 x 5 in imagePredictor: FEMA Cost Share Average
#summary statistics since it's continuous
base::summary(EDAdata_outliers$FEMACSAvg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.6750 0.9458 1.0000 0.9641 1.0000 1.0000#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = FEMACSAvg)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = FEMACSAvg)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$FEMACSAvg)
## [1] 290 44#examine graphical relationship with outcomes of interest
#look at total cost per incident month
req_fig_cs <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = FEMACSAvg)) +
geom_point(aes(y = ReqAmt),
color = "#6ece58",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Average FEMA Cost Share",
y = "Funds ($)",
subtitle = "Requested")
req_fig_cs## Warning: Removed 1 rows containing missing values (geom_point).
#repeat for obligated funds
obl_fig_cs <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = FEMACSAvg)) +
geom_point(aes(y = OblAmt),
color = "#26828e",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Average FEMA Cost Share",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_cs## Warning: Removed 1 rows containing missing values (geom_point).
#combine into one plot
plot_col6 <- cowplot::plot_grid(req_fig_cs,
obl_fig_cs,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).plot_col6
title7 <- cowplot::ggdraw() +
draw_label(
"Requested vs obligated FEMA funds by avg FEMA cost share (Under $1B)",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_cs <- cowplot::plot_grid(
title7, plot_col6,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
funds_fig_cs
#save file
funds_cs_fig_file = here("results","funds-cs.png")
ggsave(filename = funds_cs_fig_file, plot = funds_fig_cs)## Saving 7 x 5 in imagePredictor: IH Program
#convert to factor
EDAdata_outliers$IH <- as.factor(EDAdata_outliers$IH)
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$IH, report.nas = FALSE)## Frequencies
## EDAdata_outliers$IH
## Label: Individuals/Households Program Awarded
## Type: Factor
##
## Freq % % Cum.
## ----------------- ------ -------- --------
## Not Awarded 160 49.54 49.54
## Awarded 163 50.46 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_IH <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = IH, y = OblAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
labs(x = "IH Program",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_IH
#repeat for requested funds
req_fig_IH <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = IH, y = ReqAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
labs(x = " ",
y = "Funds ($)",
subtitle = "Requested")
req_fig_IH
#combine into one plot
plot_IH <- cowplot::plot_grid(req_fig_IH,
obl_fig_IH,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_IH
title_IH <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds By IH Program Awardance",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_IH_funds <- cowplot::plot_grid(
title_IH, plot_IH,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_IH_funds
#save file
IH_fig_file = here("results","IH-funds.png")
ggsave(filename = IH_fig_file, plot = plot_IH_funds)## Saving 7 x 5 in image#create a table where rows represent requested and obligated funds and columns are declaration type
decs_ih <- EDAdata_outliers %>%
gtsummary::select(IH, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = IH,
label = list(IH ~ "Individuals / Households Program",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_header(update = list(
stat_1 ~ "**Not Awarded**",
stat_2 ~ "**Awarded**")) %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Individuals/Households Program**") %>%
gtsummary::modify_caption("**Table 5. FEMA Funding by IH Program Awardance 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_ih| Variable | Individuals/Households Program | |
|---|---|---|
| Not Awarded1 | Awarded1 | |
| Requested Funds | 499,887 (956,929) | 80,712,926 (356,868,389) |
| Obligated Funds | 499,887 (956,929) | 80,905,614 (358,341,553) |
|
1
Mean (SD)
|
||
#save file
decs_ih_file = here("results", "decs-IH.Rds")
saveRDS(decs_ih, file = decs_ih_file)Predictor: PA Program
#convert to factor
EDAdata_outliers$PA <- as.factor(EDAdata_outliers$PA)
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$PA, report.nas = FALSE)## Frequencies
## EDAdata_outliers$PA
## Label: Public Assistance Program Awarded
## Type: Factor
##
## Freq % % Cum.
## ----------------- ------ -------- --------
## Not Awarded 8 2.48 2.48
## Awarded 315 97.52 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_PA <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = PA, y = OblAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
labs(x = "PA Program",
y = "Funds ($)",
subtitle = "FEMA funds obligated when PA program is awarded")
obl_fig_PA
#repeat for requested funds
req_fig_PA <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = PA, y = ReqAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
labs(x = " ",
y = "Funds ($)",
title = "Requested") +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
req_fig_PA
#combine into one plot
plot_PA <- cowplot::plot_grid(req_fig_PA,
obl_fig_PA,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_PA
title_PA <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds By PA Program Awardance",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_PA_funds <- cowplot::plot_grid(
title_PA, plot_PA,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_PA_funds
#save file
PA_fig_file = here("results","PA-funds.png")
ggsave(filename = PA_fig_file, plot = plot_PA_funds)## Saving 7 x 5 in image#create a table where rows represent requested and obligated funds and columns are declaration type
decs_pa <- EDAdata_outliers %>%
gtsummary::select(PA, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = PA,
label = list(PA ~ "Public Assistance Program",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_header(update = list(
stat_1 ~ "**Not Awarded**",
stat_2 ~ "**Awarded**")) %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Public Assistance Program**") %>%
gtsummary::modify_caption("**Table 6. FEMA Funding by PA Program Awardance 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_pa| Variable | Public Assistance Program | |
|---|---|---|
| Not Awarded1 | Awarded1 | |
| Requested Funds | 243,522 (419,425) | 42,013,463 (259,455,341) |
| Obligated Funds | 243,522 (419,425) | 42,113,171 (260,515,668) |
|
1
Mean (SD)
|
||
#save file
decs_pa_file = here("results", "decs-PA.Rds")
saveRDS(decs_pa, file = decs_pa_file)Predictor: HM Program
#convert to factor
EDAdata_outliers$HM <- as.factor(EDAdata_outliers$HM)
#frequency table since it's categorical
#hide NAs because there are no NAs
summarytools::freq(EDAdata_outliers$HM, report.nas = FALSE)## Frequencies
## EDAdata_outliers$HM
## Label: Hazard Mitigation Program
## Type: Factor
##
## Freq % % Cum.
## ----------------- ------ -------- --------
## Not Awarded 64 19.81 19.81
## Awarded 259 80.19 100.00
## Total 323 100.00 100.00#examine graphical relationship with outcomes
#include a jitter function to have a better idea of number of measurements and distribution
#start with obligated amount
obl_fig_HM <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = HM, y = OblAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#26828e") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
labs(x = "HM Program",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_HM
#repeat for requested funds
req_fig_HM <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = HM, y = ReqAmt)) +
geom_boxplot() +
geom_jitter(alpha = 0.5, width = 0.2, height = 0.2, color = "#6ece58") +
scale_y_continuous(labels = scales::dollar_format()) +
scale_x_discrete(labels = c("Not Awarded", "Awarded")) +
labs(x = " ",
y = "Funds ($)",
subtitle = "Requested")
req_fig_HM
#combine into one plot
plot_HM <- cowplot::plot_grid(req_fig_HM,
obl_fig_HM,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))
plot_HM
title_HM <- cowplot::ggdraw() +
draw_label(
"Requested vs Obligated FEMA Funds By HM Program Awardance",
fontface = 'bold',
x = 0,
hjust = 0) +
theme(plot.margin = margin(0, 0, 0, 7))
plot_HM_funds <- cowplot::plot_grid(
title_HM, plot_HM,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
plot_HM_funds
#save file
PA_fig_file = here("results","PA-funds.png")
ggsave(filename = PA_fig_file, plot = plot_PA_funds)## Saving 7 x 5 in image#create a table where rows represent requested and obligated funds and columns are declaration type
decs_hm <- EDAdata %>%
gtsummary::select(HM, ReqAmt, OblAmt) %>%
gtsummary::tbl_summary(
by = HM,
label = list(HM ~ "Hazard Mitigation Program",
ReqAmt ~ "Requested Funds",
OblAmt ~ "Obligated Funds"),
statistic = list(all_continuous() ~ "{mean} ({sd})")) %>%
gtsummary::modify_header(label ~ "**Variable**") %>%
gtsummary::modify_header(update = list(
stat_1 ~ "**Not Awarded**",
stat_2 ~ "**Awarded**")) %>%
gtsummary::modify_spanning_header(c("stat_1", "stat_2") ~ "**Hazard Mitigation Program**") %>%
gtsummary::modify_caption("**Table 7. FEMA Funding by HM Program Awardance 2012 - 2021**") %>%
gtsummary::bold_labels()
decs_hm| Variable | Hazard Mitigation Program | |
|---|---|---|
| Not Awarded1 | Awarded1 | |
| Requested Funds | 495,237 (742,966) | 50,782,705 (284,903,408) |
| Obligated Funds | 495,237 (742,966) | 50,903,506 (286,070,343) |
|
1
Mean (SD)
|
||
#save file
decs_hm_file = here("results", "decs-HM.Rds")
saveRDS(decs_hm, file = decs_hm_file)Predictor: Response Duration
#summary statistics since it's continuous
base::summary(EDAdata_outliers$ResponseDays)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 70.04 660.00 1181.04 1404.26 2061.02 3588.00#NAs are ongoing events
#density plot
ggplot2::ggplot(data = EDAdata_outliers, aes(x = ResponseDays)) +
geom_density()
#normal boxplot
ggplot2::ggplot(data = EDAdata_outliers, aes(y = ResponseDays)) +
geom_boxplot()
##QQ plot
car::qqPlot(EDAdata_outliers$ResponseDays)
## [1] 298 100#examine graphical relationship with outcomes of interest
req_fig_resp <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = ResponseDays)) +
geom_point(aes(y = ReqAmt),
color = "#6ece58",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = " ",
y = "Funds ($)",
subtitle = "Requested")
req_fig_resp## Warning: Removed 1 rows containing missing values (geom_point).
#repeat for obligated funds
obl_fig_resp <- EDAdata_outliers %>%
ggplot2::ggplot(aes(x = ResponseDays)) +
geom_point(aes(y = OblAmt),
color = "#26828e",
show.legend = FALSE) +
scale_y_continuous(expand = c(0, 0),
limits = c(0, 1000000000),
labels = scales::dollar_format()) +
labs(x = "Response Duration (Days)",
y = "Funds ($)",
subtitle = "Obligated")
obl_fig_resp## Warning: Removed 1 rows containing missing values (geom_point).
#combine into one plot
plot_col7 <- cowplot::plot_grid(req_fig_resp,
obl_fig_resp,
ncol = 1,
align = "v",
rel_heights = c(0.5,0.5))## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).plot_col7
title8 <- cowplot::ggdraw() +
draw_label(
"Requested vs obligated FEMA funds by response duration (Under $1B)",
fontface = 'bold',
x = 0,
hjust = 0) +
theme( plot.margin = margin(0, 0, 0, 7))
funds_fig_resp <- cowplot::plot_grid(
title8, plot_col7,
ncol = 1,
# rel_heights values control vertical title margins
rel_heights = c(0.1, 1))
funds_fig_resp
#save file
funds_resp_fig_file = here("results","funds-resp.png")
ggsave(filename = funds_resp_fig_file, plot = funds_fig_resp)## Saving 7 x 5 in imageCreate “Table 1”
Make the summary table for the manuscript. Variables to include:
- state
- IncidentYear
- IncidentMonth
- declarationType
- incidentType
- IncidentDuration
- ResponseDays
- IH Percent
- PA Percent
- HM Percent
- Population
- Counties
- TotalAgencies
- FEMACSAvg
#specify units
table1::units(EDAdata$IncidentDuration) <- "days"
table1::units(EDAdata$ResponseDays) <- "days"
#create table
table1 <- table1::table1(~ ReqAmt + OblAmt + FEMACSAvg + TotalAgencies + IH_pct + PA_pct + HM_pct + ResponseDays +
IncidentDuration + IncidentMonth + IncidentYear + incidentType + declarationType +
FEMARegion + Counties + Population, data = EDAdata)
table1| Overall (N=326) |
|
|---|---|
| Requested FEMA Funds | |
| Mean (SD) | 40600000 (255000000) |
| Median [Min, Max] | 420000 [0.000000000000455, 4390000000] |
| Obligated FEMA Funds | |
| Mean (SD) | 40700000 (256000000) |
| Median [Min, Max] | 434000 [0.000000000000455, 4410000000] |
| Average FEMA Cost Share | |
| Mean (SD) | 0.964 (0.0573) |
| Median [Min, Max] | 1.00 [0.675, 1.00] |
| Federal Agencies Involved | |
| Mean (SD) | 6.23 (7.03) |
| Median [Min, Max] | 3.00 [1.00, 55.0] |
| Percent of Counties Awarded IH | |
| Mean (SD) | 0.338 (1.59) |
| Median [Min, Max] | 0.00197 [0, 27.7] |
| Percent of Counties Awarded PA | |
| Mean (SD) | 0.594 (1.56) |
| Median [Min, Max] | 0.357 [0, 27.3] |
| Percent of Counties Awarded HM | |
| Mean (SD) | 0.476 (1.56) |
| Median [Min, Max] | 0.207 [0, 27.3] |
| Response Duration (days) | |
| Mean (SD) | 1400 (898) |
| Median [Min, Max] | 1180 [70.0, 3590] |
| Incident Duration (days) | |
| Mean (SD) | 136 (250) |
| Median [Min, Max] | 14.0 [0, 660] |
| Declaration Month | |
| January | 71 (21.8%) |
| February | 22 (6.7%) |
| March | 22 (6.7%) |
| April | 24 (7.4%) |
| May | 22 (6.7%) |
| June | 25 (7.7%) |
| July | 13 (4.0%) |
| August | 32 (9.8%) |
| September | 31 (9.5%) |
| October | 41 (12.6%) |
| November | 9 (2.8%) |
| December | 14 (4.3%) |
| Incident Year | |
| Mean (SD) | 2020 (2.89) |
| Median [Min, Max] | 2020 [2010, 2020] |
| Incident Type | |
| Biological | 60 (18.4%) |
| Earthquake | 4 (1.2%) |
| Fire | 20 (6.1%) |
| Flood | 61 (18.7%) |
| Hurricane | 73 (22.4%) |
| Other | 15 (4.6%) |
| Severe Ice Storm | 4 (1.2%) |
| Severe Storm(s) | 82 (25.2%) |
| Tornado | 7 (2.1%) |
| Declaration Type | |
| DR | 261 (80.1%) |
| EM | 65 (19.9%) |
| FEMA Region | |
| Region I | 27 (8.3%) |
| Region II | 19 (5.8%) |
| Region III | 25 (7.7%) |
| Region IV | 62 (19.0%) |
| Region IX | 31 (9.5%) |
| Region V | 37 (11.3%) |
| Region VI | 54 (16.6%) |
| Region VII | 20 (6.1%) |
| Region VIII | 24 (7.4%) |
| Region X | 27 (8.3%) |
| Counties per State | |
| Mean (SD) | 75.3 (53.5) |
| Median [Min, Max] | 67.0 [1.00, 254] |
| State Population | |
| Mean (SD) | 9310000 (10300000) |
| Median [Min, Max] | 5710000 [577000, 39500000] |
#save file
table1_file = here("results", "table1.Rds")
saveRDS(table1, file = table1_file)Map Figure
Make the following four maps:
- FEMA Regions
- Density by total number of declarations
- Density by total requested funds
- Density by obligated funds
#pull geographic boundaries from maps package
MainStates <- map_data("state")
#fix capitalization issue in map data
MainStates$name <- str_to_title(MainStates$region)
#create summary df by state for EDAdata_analysis
#count number of declarations per state
EDA_sum_1 <- EDAdata_outliers %>%
dplyr::group_by(name,
state,
latitude,
longitude,
Population,
Counties,
FEMARegion) %>%
dplyr::count(state)
#find total of requested and obligated funds
EDA_sum_2 <- EDAdata_outliers %>%
dplyr::group_by(name) %>%
dplyr::summarize(RequestedAmt = sum(ReqAmt),
ObligatedAmt = sum(OblAmt))
#combine the two
EDA_sums <- inner_join(EDA_sum_1, EDA_sum_2, by = "name")
#merge MainStates and EDAdata_outliers
MergedStates <- inner_join(MainStates, EDA_sums, by = "name")
#rename count variable
MapsData <- MergedStates %>%
dplyr::mutate(NumDecs = n) %>%
dplyr::select(-n)
#create map with FEMA Regions
map1 <- ggplot(MapsData, aes(x = long,
y = lat,
group = group)) +
geom_polygon(aes(fill = FEMARegion), colour = alpha("white", 1 / 2), size = 0.2) +
geom_polygon(data = MergedStates, colour = "white", fill = NA) +
coord_fixed() +
scale_fill_manual(values = c("#fde725", "#b5de2b", "#6ece58", "#35b779", "#1f9e89", "#26828e", "#31688e",
"#3e4989", "#482878", "#440154")) +
theme(axis.line = element_blank(), axis.text = element_blank(),
axis.ticks = element_blank(), axis.title = element_blank())
map1
#save file
map1_fig_file = here("results","map1.png")
ggsave(filename = map1_fig_file, plot = map1)## Saving 7 x 5 in image#create map with number of declarations
map2 <- ggplot(MapsData, aes(x = long,
y = lat,
group = group)) +
geom_polygon(aes(fill = NumDecs), colour = alpha("white", 1 / 2), size = 0.2) +
geom_polygon(data = MergedStates, colour = "white", fill = NA) +
coord_fixed() +
scale_fill_viridis_c(option = "D",
name = "Number of Declarations") +
theme(axis.line = element_blank(), axis.text = element_blank(),
axis.ticks = element_blank(), axis.title = element_blank())
map2
#save file
map2_fig_file = here("results","map2.png")
ggsave(filename = map2_fig_file, plot = map2)## Saving 7 x 5 in image#create map with requested amount
map3 <- ggplot(MapsData, aes(x = long,
y = lat,
group = group)) +
geom_polygon(aes(fill = RequestedAmt), colour = alpha("white", 1 / 2), size = 0.2) +
geom_polygon(data = MergedStates, colour = "white", fill = NA) +
coord_fixed() +
scale_fill_viridis_c(option = "D",
labels=scales::dollar_format(),
name = "Requested Amount") +
theme(axis.line = element_blank(), axis.text = element_blank(),
axis.ticks = element_blank(), axis.title = element_blank())
map3
#save file
map3_fig_file = here("results","map3.png")
ggsave(filename = map3_fig_file, plot = map3)## Saving 7 x 5 in image#create map with obligated amount
map4 <- ggplot(MapsData, aes(x = long,
y = lat,
group = group)) +
geom_polygon(aes(fill = ObligatedAmt), colour = alpha("white", 1 / 2), size = 0.2) +
geom_polygon(data = MergedStates, colour = "white", fill = NA) +
coord_fixed() +
scale_fill_viridis_c(option = "D",
labels=scales::dollar_format(),
name = "Obligated Amount") +
theme(axis.line = element_blank(), axis.text = element_blank(),
axis.ticks = element_blank(), axis.title = element_blank())
map4
#save file
map4_fig_file = here("results","map4.png")
ggsave(filename = map4_fig_file, plot = map4)## Saving 7 x 5 in image